Question: Forecasting customer buying patterns using payment data records.

In the realm of social media and location-based services, platforms like Twitter have become a rich source of data, providing a unique opportunity to delve into the spatio-temporal behavior and preferences of users. This paper presents a probabilistic model, aptly named W4 (a shorthand for Who+Where+When+What), which harnesses the wealth of data available from short text messages, geographic locations, posting times, and user identities to uncover individual users' mobility patterns from spatial, temporal, and activity perspectives. Our model represents a pioneering effort in simultaneously analyzing user mobility from these three dimensions. The applications of W4 are multifaceted, encompassing user profiling and location prediction, and it is particularly adept at addressing questions like "Given a tweet and its posting time, can we accurately predict the user's location?" Rigorous testing on two real-world datasets reveals the model's efficacy in identifying users' spatio-temporal topics, demonstrating a significant outperformance of existing state-of-the-art baselines in the location prediction task for tweets.

**Question**: Customer Purchase Behavior Prediction from Payment Datasets

**Documents**: Predicting Consumer Behavior: A Deep Learning Approach to Analyze Payment Patterns
In the era of big data, payment datasets have emerged as a crucial resource for understanding consumer behavior. This study introduces a deep learning model designed to analyze payment patterns and predict consumer behavior. By leveraging recurrent neural networks (RNNs), which are adept at handling sequential data, our model captures the temporal dynamics of payment transactions. The model is trained on a large dataset of anonymized payment records, encompassing various transaction types and frequencies. Our approach goes beyond simple classification or regression tasks; it aims to uncover hidden patterns and trends in consumer spending habits. The predictive power of the model is validated through a series of experiments, where it consistently outperforms traditional machine learning algorithms. The insights gained from our model can inform marketing strategies, improve customer segmentation, and enable personalized recommendations in the financial sector.

**Similar Documents**: Unveiling Consumer Trends: A Deep Learning Framework for Payment Data Analysis
The proliferation of digital payments has transformed payment datasets into a treasure trove for insights into consumer behavior. This paper presents a cutting-edge deep learning framework, specifically tailored to analyze payment data and forecast consumer actions. Employing the advanced capabilities of recurrent neural networks (RNNs), the model adeptly processes the sequential nature of payment transactions, identifying the underlying temporal patterns. Trained on a vast corpus of anonymized payment records, which include diverse transaction types and frequencies, the model transc

In Thailand, investigator can track and find the suspects by using call logs from suspects' phone numbers and their contacts. In many cases, the suspects changed their phone numbers to avoid tracking. The problem is that the investigators have difficulty to track these suspects from their call logs. Our hypothesis is that each user has a unique calling behavior pattern. The calling pattern is importance for tracking suspect's telephone number. To compare the calling patterns, we consider common contact groups. Thus, the aim of this project is to develop a call logs tracking system which can predict a set of new possible suspect's phone numbers and present their contacts' connection with our network diagram visualization based on Graph database (Neo4j). This system will be very necessary for investigators because it can save investigators' time from analyzing excessive call logs data. The system can predict the possible suspect's phone numbers. Furthermore, our visualization can enhance human's sight ability to connect the relation among related phone numbers. Finally, the experimental results on real call logs demonstrate that our method can track telephone number approximately 69% of single possible suspect phone number's matching while 89% of multiple possible suspect phone numbers' matching.

This paper presents and analysis the common existing sequential pattern mining algorithms. It presents a classifying study of sequential pattern-mining algorithms into five extensive classes. First, on the basis of Apriori-based algorithm, second on Breadth First Search-based strategy, third on Depth First Search strategy, fourth on sequential closed-pattern algorithm and five on the basis of incremental pattern mining algorithms. At the end, a comparative analysis is done on the basis of important key features supported by various algorithms. This study gives an enhancement in the understanding of the approaches of sequential

In the era of social commerce, users often connect from e-commerce websites to social networking venues such as Facebook and Twitter. However, there have been few efforts on understanding the correlations between users' social media profiles and their e-commerce behaviors. This paper presents a system for predicting a user's purchase behaviors on e-commerce websites from the user's social media profile. We specifically aim at understanding if the user's profile information in a social network (for example Facebook) can be leveraged to predict what categories of products the user will buy from (for example eBay Electronics). The paper provides an extensive analysis on how users' Facebook profile information correlates to purchases on eBay, and analyzes the performance of different feature sets and learning algorithms on the task of purchase behavior prediction.

The banking industry has undergone various changes in the way they conduct the business and focus on modern technologies to compete the market. The banking industry has started realizing the importance of creating the knowledge base and its utilization for the benefits of the bank in the area of strategic planning to survive in the competitive market. In the modern era, the technologies are advanced and it facilitates to generate, capture and store data are increased enormously. Data is the most valuable asset, especially in financial industries. The value of this asset can be evaluated only if the organization can extract the valuable knowledge hidden in raw data. The increase in the huge volume of data as a part of day to day operations and through other internal and external sources, forces information technology industries to use technologies like data mining to transform knowledge from data. Data mining technology provides the facility to access the right information at the right time from huge volumes of raw data. Banking industries adopt the data mining technologies in various areas especially in customer segmentation and profitability, Predictions on Prices/Values of different investment products, money market business, fraudulent transaction detections, risk predictions, default prediction on pricing. It is a valuable tool which identifies potentially useful information from large amount of data, from which organization can gain a clear advantage over its competitors. This study shows the significance of data mining technologies and its advantages in the banking and financial sectors.

Neural networks are good at classification, forecasting and recognition. They are also good candidates of financial forecasting tools. Forecasting is often used in the decision making process. Neural network training is an art. Trading based on neural network outputs, or trading strategy is also an art. We will discuss a seven-step neural network forecasting model building approach in this article. Pre and post data processing/analysis skills, data sampling, training criteria and model recommendation will also be covered in this article. 1. Forecasting with Neural Networks Forecasting is a process that produces a set of outputs by given a set of variables. The variables are normally historical data. Basically, forecasting assumes that future occurrences are based, at least in part, on presently observable or past events. It assumes that some aspects of the past patterns will continue into the future. Past relationships can then be discovered through study and observation. The basic idea of forecasting is to find an approximation of mapping between the input and output data in order to discover the implicit rules governing the observed movements. For instance, the forecasting of stock prices can be described in this way. Assume that i u represents today's price, i v represents the price after ten days. If the prediction of a stock price after ten days could be obtained using today's stock price, then there should be a functional mapping i u to i v , where ) ( i i i u v Γ = . Using all ( i u , i v ) pairs of historical data, a general function () Γ which consists of () i Γ could be obtained, that is ) (u v Γ = . More generally, u which consists of more information in today's price could be used in function () Γ . As NNs are universal approximators, we can find a NN simulating this () Γ function. The trained network is then used to predict the movements for the future. NN based financial forecasting has been explored for about a decade. Many research papers are published on various international journals and conferences proceedings. Some companies and institutions are also claiming or marketing the so called advanced forecasting tools or models. Some research results of financial forecasting found in references. For instance, stock trading system[4], stock forecasting [6, 22], foreign exchange rates forecasting [15, 24], option prices [25], advertising and sales volumes [13]. However, Callen et al. [3] claim that NN models are not necessarily superior to linear time series models even when the data are financial, seasonal and nonlinear. 2. Towards a Better Robust Financial Forecasting Model In working towards a more robust financial forecasting model, the following issues are worth examining. First, instead of emphasizing on the forecasting accuracy only, other financial criteria should be considered. Current researchers tend to use goodness of fit or similar criteria to judge or train their models in financial domain. In terms of mathematical calculation this approach is a correct way in theory. As we understand that a perfect forecasting is impossible in reality. No model can achieve such an ideal goal. Under this constraint, seeking a perfect forecasting is not our aim. We can only try to optimize our imperfect forecasts and use other yardsticks to give the most realistic measure. Second, there should be adequate organization and processing of forecasting data. Preprocessing and proper sampling of input data can have impact on the forecasting performance. Choice of indicators as inputs through sensitivity analysis could help to eliminate redundant inputs. Furthermore, NN forecasting results should be used wisely and effectively. For example, as the forecast is not perfect, should we compare the NN output with the previous forecast or with the real data especially when price levels are used as the forecasting targets? Third, a trading system should be used to decide on the best tool to use. NN is not the single tool that can be used for financial forecasting. We also cannot claim that it is the best forecasting tool. In fact, people are still not aware of which kind of time series is the most suitable for NN applications. To conduct post forecasting analysis will allow us to find out the suitability of models and series. We may then conclude that a certain kind of models should be used for a certain kind of time series. Training or building NN models is a trial and error procedure. Some researchers are not willing to test more on their data set [14]. If there is a system that can help us to formalize these tedious exploratory procedures, it will certainly be of great value to financial forecasting. Instead of just presenting one successful experiment, possibility or confidence level can be applied to the outputs. Data are partitioned into several sets to find out the particular knowledge of this time series. As stated by David Wolpert and William Macready about their No-Free-Lunch theorems [28], averaged over all problems, all search algorithms perform equally. Just experimenting on a single data set, a NN model which outperforms other models can be found. However, for another data set one model which outperforms NN model can also be found according to No-Free-Lunch theorems. To avoid such a case of one model outperforming others, we partition the data set into several sub data sets. The recommended NN models are those that outperform other models for all sub time horizons. In other words, only those models incorporated with enough local knowledge can be used for future forecasting. It is very important and necessary to emphasize these three issues here. Different criteria exist for the academics and the industry. In academics, sometime people seek for the accuracy towards 100%. While in industry a guaranteed 60% accuracy is typically aimed for. In addition, profit is the eventual goal of practitioners, so a profit oriented forecasting model may fit their needs. Cohen [5] surveyed 150 papers in the proceedings of the 8th National Conference on artificial intelligence. He discovered that only 42% of the papers reported that a program had run on more than one example; just 30% demonstrated performance in some way; a mere 21% framed hypotheses or made predictions. He then concluded that the methodologies used were incomplete with respect to the goals of designing and analyzing AI system. Tichy [20] showed that in a very large study of over 400 research articles in computer science. Over 40% of the articles are about new designs and the models completely lack experimental data. In a recent IEEE computer journal, he also points out 16 excuses to avoid experimentation for computer scientists [21]. What he is talking is true and not a joke. Prechelt [14] showed that the situation is not better in the NN literature. Out of 190 papers published in wellknown journals dedicated to NNs, 29% did not employ even a single realistic or real learning problem. Only 8% of the articles presented results for more than one problem using real world data. To build a NN forecasting we need sufficient experiments. To test only for one market or just for one particular time period means nothing. It will not lead to a robust model based on manually, trial-and-error, or ad hoc experiments. More robust model is needed but not only in one market or for one time period. Because of the lack of industrial models and because failures in academic research are not published, a single person or even a group of researchers will not gain enough information or experiences to build up a good forecasting model. It is obvious that an automated system dealing with NN models building is necessary. 3. Steps of NN Forecasting: The Art of NN Training As NN training is an art, many searchers and practitioners have worked in the field to work towards successful prediction and classification. For instance, William Remus and Marcus O'connor [16] suggest some principles for NN prediction and classification are of critical importance in the chapter, “Principles of Forecasting” in “A Handbook for Researchers and Practitioners”: • Clean the data prior to estimating the NN model. • Scale and deseasonalize data prior to estimating the model. • Use appropriate methods to choose the right starting point. • Use specialized methods to avoid local optima. • Expand the network until there is no significant improvement in fit. • Use pruning techniques when estimating NNs and use holdout samples when evaluating NNs. • Take care to obtain software that has in-built features to avoid NN disadvantages. • Build plausible NNs to gain model acceptance by reducing their size. • Use more approaches to ensure that the NN model is valid. With the authors' experience and sharing from other researchers and practitioners, we propose a seven-step approach for NN financial forecasting model building. The seven steps are basic components of the automated system and normally involved in the manual approach. Each step deals with an important issue. They are data preprocessing, input and output selection, sensitive analysis, data organization, model construction, post analysis and model recommendation. Step 1. Data Preprocessing A general format of data is prepared. Depending on the requirement, longer term data, e.g. weekly, monthly data may also be calculated from more frequently sampled time series. We may think that it makes sense to use as frequent data sampling as possible for experiments. However, researchers have found that increasing observation frequency does not always help to improve the accuracy of forecasting [28]. Inspection of data to find outliers is also important as outliers make it difficult for NNs and other forecasting models to model the true underlying functional. Although NNs have been shown to be universal approximators, it had been found that NNs had difficulty modeling seasonal patterns in time series [11].When a time series contains significant seasonality, the data

Customer churn analysis and prediction play an important role in customer relationship management and improve benefit of enterprise. According to the bank's customer churn data which is large scale and imbalance, this paper presented a support vector machine model to predict customer churn. The method was compared with artificial neural network, decision tree, logistic regression and naive Bayesian classifier regarding customer churn prediction for a commercial bank's VIP customers. It is found that the method has the best accuracy rate, hit rate, covering rate and lift coefficient, and provides an effective measurement for bank's customer churn prediction.

In recent years, opinion mining attracted a great deal of research attention. However, limited work has been done on detecting opinion spam (or fake reviews). The problem is analogous to spam in Web search [1, 9 11]. However, review spam is harder to detect because it is very hard, if not impossible, to recognize fake reviews by manually reading them [2]. This paper deals with a restricted problem, i.e., identifying unusual review patterns which can represent suspicious behaviors of reviewers. We formulate the problem as finding unexpected rules. The technique is domain independent. Using the technique, we analyzed an Amazon.com review dataset and found many unexpected rules and rule groups which indicate spam activities.

Smallholder agriculture is vital for food security and poverty reduction. Poor, unbanked smallholder farmers face challenges in obtaining loans for agriculture due to missing financial credit history. Although credit decisions tools with alternatives such as big data from mobile phones currently exist, these are mainly targeted towards consumer credit solutions. There is need for research focused on applying such data to credit decisions in agriculture, considering the unique challenges of the field. This research proposes a credit decision tool using alternative data from a mobile phone application used for reporting agricultural activities. 213 users submitted 41613 reports of farm activity by 11336 farmers over a 5-year period. Users were classified groups based on personal information, association with farmers, and reports. Three methods are applied for analysis: multiple logistic regression, a support vector machine with a linear kernel, and a support vector machine with a radial basis function kernel. Three sets of features, selected with different methods, are used. The results are evaluated by accuracy, and Area Under the Receiver operating curve (AUC). The support vector machine with a radial basis function kernel shows the best performance on the original data set with an Area Under the Receiver operating curve (AUC) value of 0.983. Feasibility of applying each method in agricultural microfinance is considered. Finally, further development of the models using Geographic Information System analysis of geospatial data which affects agricultural productivity, such as soil type and water resources, is suggested to incorporate the ability of the borrower to repay loans from farm activity.

Cashiers in retail stores usually exhibit certain repetitive and periodic activities when processing items. Detecting such activities plays a key role in most retail fraud detection systems. In this paper, we propose a highly efficient, effective and robust vision technique to detect checkout-related primitive activities, based on a hierarchical finite state machine (FSM). Our deterministic approach uses visual features and prior spatial constraints on the hand motion to capture particular motion patterns performed in primitive activities. We also apply our approach to the problem of retail fraud detection. Experimental results on a large set of video data captured from retail stores show that our approach, while much simpler and faster, achieves significantly better results than state-of-the-art machine learning-based techniques both in detecting checkout-related activities and in detecting checkout-related fraudulent incidents.

The Internet is emerging as a new marketing channel, so understanding the characteristics of online customers’ needs and expectations is considered a prerequisite for activating the consumer-oriented electronic commerce market. In this study, we propose a novel clustering algorithm based on genetic algorithms (GAs) to effectively segment the online shopping market. In general, GAs are believed to be effective on NP-complete global optimization problems, and they can provide good near-optimal solutions in reasonable time. Thus, we believe that a clustering technique with GA can provide a way of finding the relevant clusters more effectively. The research in this paper applied K-means clustering whose initial seeds are optimized by GA, which is called GA K-means, to a real-world online shopping market segmentation case. In this study, we compared the results of GA K-means to those of a simple K-means algorithm and self-organizing maps (SOM). The results showed that GA K-means clustering may improve segmentation performance in comparison to other typical clustering algorithms. In addition, our study validated the usefulness of the proposed model as a preprocessing tool for recommendation systems. 2007 Elsevier Ltd. All rights reserved.


Question: Forecasting customer buying patterns using payment data records.

This paper delves into the statistical characteristics of individual human mobility patterns, highlighting the non-random nature of human trajectories. Despite the relevance of continuous-time random-walk (CTRW) models in capturing certain aspects of human movement, empirical evidence from mobile-phone traces reveals significant discrepancies between model predictions and real-world data. To address this gap, we propose a novel framework that incorporates two fundamental principles underlying human mobility. By adhering to these principles, our model not only aligns with the observed scaling laws but also enables the analytical prediction of key scaling exponents, offering a more accurate and self-consistent representation of individual human mobility. This approach significantly enhances our understanding of human movement, with implications for various fields including epidemic modeling, traffic forecasting, and urban planning.

**Question**: A Comparative Study of Machine Learning Techniques in Financial Market Forecasting

**Documents**: Deep learning for stock market prediction: A survey
The stock market is a complex and dynamic system, influenced by a multitude of factors, including economic indicators, political events, and investor sentiment. Forecasting stock prices accurately is crucial for investors and traders to make informed decisions. In recent years, deep learning techniques have emerged as powerful tools for stock market prediction. This paper provides a comprehensive survey of deep learning methods applied to stock market forecasting. We discuss the advantages of deep learning over traditional machine learning techniques in handling high-dimensional and non-linear data. We also review various deep learning architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks, and their applications in stock price prediction. Furthermore, we highlight the challenges and limitations of deep learning in this domain and suggest potential research directions for future work.

**Similar Documents**: This article offers an exhaustive review of deep learning methodologies employed in the prediction of stock market trends. Given the intricate nature of the stock market, influenced by a broad spectrum of factors, the accuracy of stock price forecasting is critical for effective decision-making in investment strategies. Over the past decade, deep learning algorithms have proven to be potent instruments in tackling the high-dimensionality and non-linearity inherent in financial data. We explore the superiority of deep learning over conventional machine learning approaches in managing complex datasets. Additionally, we delve into different deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks, examining their roles in stock price forecasting. The paper also addresses the obstacles and constraints associated with deep learning in financial markets and proposes

The paper presents a new method for data transformation. The obtained data format enhances the efficiency of the time series classification. The transformation is realized in four steps, namely: time series segmentation, segment's feature representation, segments' binning and pattern frequency representation. The method is independent of the nature of the data, and it works well with temporal data from diverse areas. Its ability is proved by classification of different real datasets and is also compared with the results of other classification methods.

The rapid growth of the market in every sector is leading to a bigger subscriber base for service providers. More competitors, new and innovative business models and better services are increasing the cost of customer acquisition. In this environment service providers have realized the importance of the retention of existing customers. Therefore, providers are forced to put more efforts for prediction and prevention of churn. This paper aims to present commonly used data mining techniques for the identification of churn. Based on historical data these methods try to find patterns which can point out possible churners. Well-known techniques used for this are Regression analysis, Decision Trees, Neural Networks and Rule based learning. In section 1 we give a short introduction describing the current state of the market, then in section 2 a definition of customer churn, its’ types and the imporance of identification of churners is being discussed. Section 3 reviews different techniques used, pointing out advantages and disadvantages. Finally, current state of research and new emerging algorithms are being presented.

Nowadays companies increasingly derive revenue from the creation and sustenance of long-term relationships with their customers. In such an environment, marketing serves the purpose of maximizing customer lifetime value (CLV) and customer equity, which is the sum of the lifetime values of the company’s customers. A frequently-encountered difficulty for companies wishing to measure customer profitability is that management accounting and reporting systems have tended to reflect product profitability rather than customer profitability. But in spite of these difficulties, Companies looking for methods to know how calculate their customers's CLV. In this paper, we used K-Mean clustering approach to determine customers's CLV and segment them based on recency, frequency and monetary (RFM) measures. We also used Discriminant analysis to approve clustering results. Data required applying this method gathered from one branch of an Iranian private bank which is established newly. Finally, in terms of this segmentation, we proposed customer retention strategies for treating with the bank customers.

This paper describes the automatic design of methods for detecting fraudulent behavior. Much of the design is accomplished using a series of machine learning methods. In particular, we combine data mining and constructive induction with more standard machine learning techniques to design methods for detecting fraudulent usage of cellular telephones based on profiling customer behavior. Specifically, we use a rulelearning program to uncover indicators of fraudulent behavior from a large database of cellular calls. These indicators are used to create profilers, which then serve as features to a system that combines evidence from multiple profilers to generate high-confidence alarms. Experiments indicate that this automatic approach performs nearly as well as the best hand-tuned methods for detecting fraud.

Probabilistic forecasting, i.e. estimating the probability distribution of a time series’ future given its past, is a key enabler for optimizing business processes. In retail businesses, for example, forecasting demand is crucial for having the right inventory available at the right time at the right place. In this paper we propose DeepAR, a methodology for producing accurate probabilistic forecasts, based on training an auto-regressive recurrent network model on a large number of related time series. We demonstrate how by applying deep learning techniques to forecasting, one can overcome many of the challenges faced by widely-used classical approaches to the problem. We show through extensive empirical evaluation on several real-world forecasting data sets that our methodology produces more accurate forecasts than other state-of-the-art methods, while requiring minimal manual work.

Di erent users can use a given Internet application in many different ways. The ability to record detailed event logs of user inapplication activity allows us to discover ways in which the application is being used. This enables personalization and also leads to important insights with actionable business and product outcomes. Here we study the problem of user session categorization, where the goal is to automatically discover categories/classes of user insession behavior using event logs, and then consistently categorize each user session into the discovered classes. We develop a three stage approach which uses clustering to discover categories of sessions, then builds classi ers to classify new sessions into the discovered categories, and nally performs daily classi cation in a distributed pipeline. An important innovation of our approach is selecting a set of events as long-tail features, and replacing them with a new feature that is less sensitive to product experimentation and logging changes. This allows for robust and stable identi cation of session types even though the underlying application is constantly changing. We deploy the approach to Pinterest and demonstrate its e ectiveness. We discover insights that have consequences for product monetization, growth, and design. Our solution classi es millions of user sessions daily and leads to actionable insights.

Credit scoring is a decision tool used by organizations to grant or reject credit requests from their customers. Series of artificial intelligent and traditional approaches have been used to building credit scoring model and credit risk evaluation. Despite being ranked amongst the top 10 algorithm in Data mining, Naïve Bayesian algorithm has not been extensively used in building credit score cards. Using demographic and material indicators as input variables, this paper investigate the ability of Bayesian classifier towards building credit scoring model in banking sector.

The healthcare industry is a vast field with a plethora of data about patients,added to the huge medical records every passing day. In terms of science, this industry is ’information rich’ yet ’knowledge poor’. However, data mining with its various analytical tools and techniques plays a major role in reducing the use of cumbersome tests used on patients to detect a disease. The aim of this paper is to employ and analyze different data mining techniques for the prediction of heart disease in a patient through extraction of interesting patterns from the dataset using vital parameters. This paper strives to bring out the methodology and implementation of these techniques-Artificial Neural Networks, Decision Tree and Naive Bayes and stress upon the results and conclusion induced on the basis of accuracy and time complexity. By far, the observations reveal that Artificial Neural Networks outperformed Naive Bayes and Decision Tree.

This paper illustrates the similarities between the problems of customer churn and employee turnover. An example of employee turnover prediction model leveraging classical machine learning techniques is developed. Model outputs are then discussed to design & test employee retention policies. This type of retention discussion is, to our knowledge, innovative and constitutes the main value of this paper. 2010 Mathematics Subject Classification.

Association rule mining was first proposed by Agrawal, Imielinski, and Swami [AIS93]. The Apriori algorithm discussed in Section 5.2.1 for frequent itemset mining was presented in Agrawal and Srikant [AS94b]. A variation of the algorithm using a similar pruning heuristic was developed independently by Mannila, Tiovonen, and Verkamo [MTV94]. A joint publication combining these works later appeared in Agrawal, Mannila, Srikant, Toivonen, and Verkamo [AMS96]. A method for generating association rules from frequent itemsets is described in Agrawal and Srikant [AS94a].


Question: Forecasting customer buying patterns using financial transaction records.

This paper explores the intricacies of random search methodologies, with a specific focus on superdiffusive Lévy processes and correlated random walks. Our investigation spans various scenarios, including different target site densities, mobility factors, and revisitability conditions. We present findings derived from both analytical, mean-field-like calculations and empirical, numerical simulations. Our study delves into random search dynamics on regular lattices and those with defects, outlining a criterion to distinguish genuine superdiffusion from correlated random walk phenomena. We also incorporate energy considerations, particularly in relation to critical survival states on the brink of extinction, and analyze the manifestation of Lévy behavior in deterministic search walks. Moreover, we extend our discussion to the biological foraging context, illustrating the relevance of these search patterns in nature. PACS codes: 05.50.Fb, 05.40.−a

However, in a contrasting context, this paper could be reimagined to discuss the prediction of customer purchase behavior from payment datasets, utilizing advanced analytical techniques and machine learning algorithms. In this scenario, the analysis would encompass various customer profiles, spending patterns, and purchase frequency, akin to the different target site densities and mobility factors in random search methodologies. The study would involve both statistical modeling and data-driven simulations to forecast consumer behavior accurately. Additionally, the paper would explore the impact of market trends and economic conditions on customer spending, similar to the energy considerations in the context of superdiffusion and correlated random walks. Finally, the document would highlight the predictive models' application in retail and e-commerce environments, demonstrating their efficacy in enhancing sales and customer satisfaction.

Generating accurate and reliable sales forecasts is crucial in the E-commerce business. The current state-of-theart techniques are typically univariate methods, which produce forecasts considering only the historical sales data of a single product. However, in a situation where large quantities of related time series are available, conditioning the forecast of an individual time series on past behaviour of similar, related time series can be beneficial. Given that the product assortment hierarchy in an E-commerce platform contains large numbers of related products, in which the sales demand patterns can be correlated, our attempt is to incorporate this cross-series information in a unified model. We achieve this by globally training a Long Short-Term Memory network (LSTM) that exploits the nonlinear demand relationships available in an E-commerce product assortment hierarchy. Aside from the forecasting engine, we propose a systematic pre-processing framework to overcome the challenges in an E-commerce setting. We also introduce several product grouping strategies to supplement the LSTM learning schemes, in situations where sales patterns in a product portfolio are disparate. We empirically evaluate the proposed forecasting framework on a real-world online marketplace dataset from Walmart.com. Our method achieves competitive results on category level and super-departmental level datasets, outperforming stateof-the-art techniques.

User intended actions are widely seen in many areas. Forecasting these actions and taking proactive measures to optimize business outcome is a crucial step towards sustaining the steady business growth. In this work, we focus on predicting attrition, which is one of typical user intended actions. Conventional attrition predictive modeling strategies suffer a few inherent drawbacks. To overcome these limitations, we propose a novel end-to-end learning scheme to keep track of the evolution of attrition patterns for the predictive modeling. It integrates user activity logs, dynamic and static user profiles based on multi-path learning. It exploits historical user records by establishing a decaying multi-snapshot technique. And finally it employs the precedent user intentions via guiding them to the subsequent learning procedure. As a result, it addresses all disadvantages of conventional methods. We evaluate our methodology on two public data repositories and one private user usage dataset provided by Adobe Creative Cloud. The extensive experiments demonstrate that it can offer the appealing performance in comparison with several existing approaches as rated by different popular metrics. Furthermore, we introduce an advanced interpretation and visualization strategy to effectively characterize the periodicity of user activity logs. It can help to pinpoint important factors that are critical to user attrition and retention and thus suggests actionable improvement targets for business practice. Our work will provide useful insights into the prediction and elucidation of other user intended actions as well.

Cloud computing is the one of the admired paradigms of current era, which facilitates the users with on demand services and pay as you use services. It has tremendous applications in almost every sphere such as education, gaming, social networking, transportation, medical, business, stock market, pattern matching, etc. Stock market is such an industry where lots of data is generated and benefits are reaped on the basis of accurate prediction. So prediction is a vital part of stock market. Therefore, an attempt is being made to predict the stock market based on the given data set of stock market along with some features; using the techniques available for predictive data mining. Machine learning is one of the upcoming trends of data mining; hence few machine learning algorithms have been used such as Decision tree, Linear model, Random forest and further their results have been compared using the classification evaluation parameters such as H, AUC, ROC, TPR, FPR, etc. Random forest have been consider as the most effective model as it yield the highest accuracy of 54.12% whereas decision tree and linear model gives the accuracy of 51.87% and 52.83% respectively.

In Web Usage Mining (WUM), Classification technique is useful in web user prediction, where user’s browsing behavior is predicted. In Web prediction, classification is done first to predict the next Web pages set that a user may visit based on history of previously visited pages. Predicting user’s behavior effectively is important in various applications like recommendation systems, smart phones etc. Currently various prediction systems are available based on Markov model, Association Rule Mining (ARM), classification techniques, etc. In Markov prediction model it cannot predict for a session that was not observed previously in the training set and prediction time is also constant. In ARM, Apriori algorithm requires more time to generate item sets. This paper presents review of various prediction models used to predict web users browsing behavior. Key Words— WUM, classification, Browsing behavior, prediction, ARM, session

In recent years the amount of data stored in educational database is growing rapidly. The stored database contains hidden information which if used aids improvement of student's performance and behaviour. In this paper predictive modelling approach is used for extracting this hidden information. Data is collected, a predictive model is formulated, predictions are made, and the model is validated as additional data becomes available. The predictive models will help the instructor to understand how well or how poorly the students in his/her class will perform, and hence the instructor can choose proper pedagogical and instructional interventions to enhance student learning outcomes. The implementation is done in Hadoop framework with MapReduce and Revolutionary R Enterprise RRE.

Make more knowledge even in less time every day. You may not always spend your time and money to go abroad and get the experience and knowledge by yourself. Reading is a good alternative to do in getting this desirable knowledge and experience. You may gain many things from experiencing directly, but of course it will spend much money. So here, by reading time series prediction forecasting the future and understanding the past, you can take more advantages with limited budget.

Recognizing high-level human behavior and decisions from their digital traces are critical issues in pervasive computing systems. In this paper, we develop a novel methodology to reveal cabdrivers’ operation patterns by analyzing their continuous digital traces. For the first time, we systematically study large scale cabdrivers’ behavior in a real and complex city context through their daily digital traces. We identify a set of valuable features, which are simple and effective to classify cabdrivers, delineate cabdrivers’ operation patterns and compare the different cabdrivers’ behavior. The methodology and steps could spatially and temporally quantify, visualize, and examine different cabdrivers’ operation patterns. Drivers were categorized into top drivers and ordinary drivers by their daily income. We use the daily operations of 3000 cabdrivers in over 48 million of trips and 240 million kilometers to uncover: (1) spatial selection behavior, (2) context-aware spatio-temporal operation behavior, (3) route choice behavior, and (4) operation tactics. Though we focused on cabdriver operation patterns analysis from their digital traces, the methodology is a general empirical and analytical methodology for any GPS-like trace analysis. Our work demonstrates the great potential to utilize the massive pervasive data sets to understand human behavior and high-level intelligence. Published by Elsevier Ltd.

The automated computer programs using data mining and predictive technologies do a fare amount of trades in the markets. Data mining is well founded on the theory that the historic data holds the essential memory for predicting the future direction. This technology is designed to help investors discover hidden patterns from the historic data that have probable predictive capability in their investment decisions. The prediction of stock markets is regarded as a challenging task of financial time series prediction. Data analysis is one way of predicting if future stocks prices will increase or decrease. Five methods of analyzing stocks were combined to predict if the day's closing price would increase or decrease. These methods were Typical Price (TP), Bollinger Bands, Relative Strength Index (RSI), CMI and Moving Average (MA). This paper discussed various techniques which are able to predict with future closing stock price will increase or decrease better than level of significance. Also, it investigated various global events and their issues predicting on stock markets. It supports numerically and graphically.

With the development of GPS and the popularity of smart phones and wearable devices, users can easily log their daily trajectories. Prior works have elaborated on mining trajectory patterns from raw trajectories. Trajectory patterns consist of hot regions and the sequential relationships among them, where hot regions refer the spatial regions with a higher density of data points. Note that some hot regions do not have any meaning for users. Moreover, trajectory patterns do not have explicit time information or semantic information. To enrich trajectory patterns, we propose semantic trajectory patterns which are referred to as the moving patterns with spatial, temporal, and semantic attributes. Given a user trajectory, we aim at mining frequent semantic trajectory patterns. Explicitly, we extract the three attributes from a raw trajectory, and convert it into a semantic mobility sequence. Given such a semantic mobility sequence, we propose two algorithms to discover frequent semantic trajectory patterns. The first algorithm, MB (standing for matching-based algorithm), is a naive method to find frequent semantic trajectory patterns. It generates all possible patterns and extracts the occurrence of the patterns from the semantic mobility sequence. The second algorithm, PS (standing for PrefixSpan-based algorithm), is developed to efficiently mine semantic trajectory patterns. Due to the good efficiency of PrefixSpan, algorithm PS will fully utilize the advantage of PrefixSpan. Since the semantic mobility sequence contains three attributes, we need to further transform it into a raw sequence before using algorithm PrefixSpan. Therefore, we propose the SS algorithm (standing for sequence symbolization algorithm) to achieve this purpose. To evaluate our proposed algorithms, we conducted experiments on the real datasets of Google Location History, and the experimental results show the effectiveness and efficiency of our proposed algorithms.

A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models without access to precise information in individual data records? We consider the concrete case of building a decision-tree classifier from training data in which the values of individual records have been perturbed. The resulting data records look very different from the original records and the distribution of data values is also very different from the original distribution. While it is not possible to accurately estimate original values in individual data records, we propose a novel reconstruction procedure to accurately estimate the distribution of original data values. By using these reconstructed distributions, we are able to build classifiers whose accuracy is comparable to the accuracy of classifiers built with the original data.


Question: Forecasting customer buying patterns using financial transaction records.

This paper introduces a novel method for enhancing Point-of-interest (POI) recommendation in location-based social networks (LBSNs) by integrating geographical and social influence factors into matrix factorization. With the rise of platforms like Gowalla, Foursquare, Facebook, and Brightkite, users are increasingly sharing their social connections and locations through check-ins. This data provides a rich source for mining user preferences and offering personalized recommendations. Matrix factorization, a technique proven effective in recommendation systems, is leveraged here to address the POI recommendation challenge. However, existing matrix factorization approaches often overlook geographical influence, such as the tendency for users to check in at multiple centers, leading to less optimal recommendations. In this paper, we propose a first-of-its-kind framework that merges matrix factorization with geographical and social influence for improved POI recommendations in LBSNs. Initially, we model the likelihood of a user checking in at a specific location using a Multi-center Gaussian Model (MGM) to capture geographical influence. Then, we incorporate social data and integrate the geographical influence into a generalized matrix factorization framework. This approach results in an efficient POI recommendation system that scales linearly with the number of check-ins. Extensive experimentation on a large, real-world LBSN dataset confirms the effectiveness of our method, demonstrating its significant superiority over existing state-of-the-art techniques in utilizing distance information for recommendations.

Although frequent-pattern mining has been widely studied and used, it is challenging to extend it to data streams. Compared to mining from a static transaction data set, the streaming case has far more information to track and far greater complexity to manage. Infrequent items can become frequent later on and hence cannot be ignored. The storage structure needs to be dynamically adjusted to reflect the evolution of itemset frequencies over time. In this paper, we propose computing and maintaining all the frequent patterns (which is usually more stable and smaller than the streaming data) and dynamically updating them with the incoming data streams. We extended the framework to mine time-sensitive patterns with approximate support guarantee. We incrementally maintain tilted-time windows for each pattern at multiple time granularities. Interesting

Data Mining is taking out of hidden patterns from huge database. It is commonly used in a marketing, surveillance, fraud detection and scientific discovery. In data mining, machine learning is mainly focused as research which is automatically learnt to recognize complex patterns and make intelligent decisions based on data. Nowadays traffic accidents are the major causes of death and injuries in this world. Roadway patterns are useful in the development of

Categorizing visitors based on their interactions with a we bsite is a key problem in web usage mining. The clickstreams generated by various users often follow disti nct patterns, the knowledge of which may help in providing customized content. In this paper, we propose a novel and eff ective algorithm for clustering webusers based on a function of the longest common subsequence of their clickst reams that takes into account both the trajectory taken through a website and the time spent at each page. Results are presented on weblogs of www.sulekha.com to illustrate the techniques. keywords : web usage mining, clickstream, subsequence, clustering.

Recognizing high-level human behavior and decisions from their digital traces are critical issues in pervasive computing systems. In this paper, we develop a novel methodology to reveal cabdrivers’ operation patterns by analyzing their continuous digital traces. For the first time, we systematically study large scale cabdrivers’ behavior in a real and complex city context through their daily digital traces. We identify a set of valuable features, which are simple and effective to classify cabdrivers, delineate cabdrivers’ operation patterns and compare the different cabdrivers’ behavior. The methodology and steps could spatially and temporally quantify, visualize, and examine different cabdrivers’ operation patterns. Drivers were categorized into top drivers and ordinary drivers by their daily income. We use the daily operations of 3000 cabdrivers in over 48 million of trips and 240 million kilometers to uncover: (1) spatial selection behavior, (2) context-aware spatio-temporal operation behavior, (3) route choice behavior, and (4) operation tactics. Though we focused on cabdriver operation patterns analysis from their digital traces, the methodology is a general empirical and analytical methodology for any GPS-like trace analysis. Our work demonstrates the great potential to utilize the massive pervasive data sets to understand human behavior and high-level intelligence. Published by Elsevier Ltd.

In this paper, various enhanced sales forecast methodologies and models for the automobile market are presented. The methods used deliver highly accurate predictions while maintaining the ability to explain the underlying model at the same time. The representation of the economic training data is discussed, as well as its effects on the newly registered automobiles to be predicted. The methodology mainly consists of time series analysis and classical Data Mining algorithms, whereas the data is composed of absolute and/or relative market-specific exogenous parameters on a yearly, quarterly, or monthly base. It can be concluded that the monthly forecasts were especially improved by this enhanced methodology using absolute, normalized exogenous parameters. Decision Trees are considered as the most suitable method in this case, being both accurate and explicable. The German and the US-American automobile market are presented for the evaluation of the forecast models.

— To classify data mining problems and algorithms we used two dimensions: data type and type of mining operations. One of the main issue that arise during the data mining process is treating data that contains temporal information. The area of temporal data mining has very much attention in the last decade because from the time related feature of the data, one can extract much significant information which can not be extracted by the general methods of data mining. Many interesting techniques of temporal data mining were proposed and shown to be useful in many applications. Since temporal data mining brings together techniques from different fields such as databases, statistics and machine learning the literature is scattered among many different sources. In this paper, we present a survey on techniques of temporal data mining.

This article describes a novel and fast recommender system for websites based on product taxonomy and user click patterns. The proposed system consists of the following four steps. First, a product-preference matrix for each customer is estimated through a linear combination of the click, basket placement, and purchase statuses. Second, the preference matrix of the genre and that of the specific type are constructed by computing the ratios of the number of clicks, basket placements, and purchases of a product to the totals. Third, cluster analysis is performed using the genre preference matrix, and a neighborhood formation process is conducted using the specific-type preference matrix. Finally, data are generated for prediction, in which a customer’s preference for specific types is greater than a given threshold value. Using these data sets, computational burden and processing time are greatly reduced. The effectiveness of the proposed approach was assessed by applying the F1 metric to an experimental e-commerce website. The proposed method outperformed conventional methods.

The presented paper modeled and predicted China stock returns using LSTM. The historical data of China stock market were transformed into 30-days-long sequences with 10 learning features and 3-day earning rate labeling. The model was fitted by training on 900000 sequences and tested using the other 311361 sequences. Compared with random prediction method, our LSTM model improved the accuracy of stock returns prediction from 14.3% to 27.2%. The efforts demonstrated the power of LSTM in stock market prediction in China, which is mechanical yet much more unpredictable.

In Web Usage Mining (WUM), Classification technique is useful in web user prediction, where user’s browsing behavior is predicted. In Web prediction, classification is done first to predict the next Web pages set that a user may visit based on history of previously visited pages. Predicting user’s behavior effectively is important in various applications like recommendation systems, smart phones etc. Currently various prediction systems are available based on Markov model, Association Rule Mining (ARM), classification techniques, etc. In Markov prediction model it cannot predict for a session that was not observed previously in the training set and prediction time is also constant. In ARM, Apriori algorithm requires more time to generate item sets. This paper presents review of various prediction models used to predict web users browsing behavior. Key Words— WUM, classification, Browsing behavior, prediction, ARM, session

Mobility prediction is one of the most essential issues that need to be explored for mobility management in mobile computing systems. In this paper, we propose a new algorithm for predicting the next inter-cell movement of a mobile user in a Personal Communication Systems network. In the first phase of our threephase algorithm, user mobility patterns are mined from the history of mobile user trajectories. In the second phase, mobility rules are extracted from these patterns, and in the last phase, mobility predictions are accomplished by using these rules. The performance of the proposed algorithm is evaluated through simulation as compared to two other prediction methods. The performance results obtained in terms of Precision and Recall indicate that our method can make more accurate predictions than the other methods. 2004 Elsevier B.V. All rights reserved.


Question: Forecasting customer buying patterns using financial transaction records.

This study delves into the intricate dynamics of human movement across geographical landscapes, identifying its pivotal role in various spatiotemporal phenomena, such as the synchronization and diversification of interacting species and human populations. Particularly significant in today's interconnected world, with growing international trade, enhanced mobility, and the looming risk of global pandemics like influenza A, understanding the statistical and dynamical aspects of human travel becomes imperative. Traditionally, models have often oversimplified these dynamics, assuming diffusive dispersion. This research, however, presents a comprehensive and quantitative analysis of human travel patterns by scrutinizing the movement of banknotes within the United States. Drawing from an extensive dataset comprising over a million individual movements, the study reveals two anomalous characteristics of human dispersal. Firstly, the distribution of travel distances follows a power law, suggesting that banknotes' journeys resemble scale-free random walks, akin to Lévy flights. Secondly, the probability of staying within a confined geographical area for a duration T is characterized by long algebraic tails, which counteracts the tendency for superdiffusive spreading. The research concludes that human travel across geographical scales can be accurately modeled using a two-parameter continuous-time random walk, demonstrating that human mobility is an ambivalent and effectively superdiffusive process, with implications that extend beyond the mere circulation of currency. This nuanced understanding of human movement patterns can inform predictive models in various fields, including epidemiology, urban planning, and economic forecasting.

T rapid advance in information technology now makes it feasible for sellers to condition their price offers on consumers’ prior purchase behavior. In this paper we examine when it is profitable to engage in this form of price discrimination when consumers can adopt strategies to protect their privacy. Our baseline model involves rational consumers with constant valuations for the goods being sold and a monopoly merchant who can commit to a pricing policy. Applying results from the prior literature, we show that although it is feasible to price so as to distinguish high-value and low-value consumers, the merchant will never find it optimal to do so. We then consider various generalizations of this model, such as allowing the seller to offer enhanced services to previous customers, making the merchant unable to commit to a pricing policy, and allowing competition in the marketplace. In these cases we show that sellers will, in general, find it profitable to condition prices on purchase history.

A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models without access to precise information in individual data records? We consider the concrete case of building a decision-tree classifier from training data in which the values of individual records have been perturbed. The resulting data records look very different from the original records and the distribution of data values is also very different from the original distribution. While it is not possible to accurately estimate original values in individual data records, we propose a novel reconstruction procedure to accurately estimate the distribution of original data values. By using these reconstructed distributions, we are able to build classifiers whose accuracy is comparable to the accuracy of classifiers built with the original data.

T paper develops a model of conversion behavior (i.e., converting store visits into purchases) that predicts each customer’s probability of purchasing based on an observed history of visits and purchases. We offer an individual-level probability model that allows for different forms of customer heterogeneity in a very flexible manner. Specifically, we decompose an individual’s conversion behavior into two components: one for accumulating visit effects and another for purchasing threshold effects. Each component is allowed to vary across households as well as over time. Visit effects capture the notion that store visits can play different roles in the purchasing process. For example, some visits are motivated by planned purchases, while others are associated with hedonic browsing (akin to window shopping); our model is able to accommodate these (and several other) types of visit-purchase relationships in a logical, parsimonious manner. The purchasing threshold captures the psychological resistance to online purchasing that may grow or shrink as a customer gains more experience with the purchasing process at a given website. We test different versions of the model that vary in the complexity of these two key components and also compare our general framework with popular alternatives such as logistic regression. We find that the proposed model offers excellent statistical properties, including its performance in a holdout validation sample, and also provides useful managerial diagnostics about the patterns underlying online buyer behavior.

World Wide Web is a huge repository of web pages and links. It provides abundance of information for the Internet users. The growth of web is tremendous as approximately one million pages are added daily. Users’ accesses are recorded in web logs. Because of the tremendous usage of web, the web log files are growing at a faster rate and the size is becoming huge. Web data mining is the application of data mining techniques in web data. Web Usage Mining applies mining techniques in log data to extract the behavior of users which is used in various applications like personalized services, adaptive web sites, customer profiling, prefetching, creating attractive web sites etc., Web usage mining consists of three phases preprocessing, pattern discovery and pattern analysis. Web log data is usually noisy and ambiguous and preprocessing is an important process before mining. For discovering patterns sessions are to be constructed efficiently. This paper reviews existing work done in the preprocessing stage. A brief overview of various data mining techniques for discovering patterns, and pattern analysis are discussed. Finally a glimpse of various applications of web usage mining is also presented. KeywordsData Cleaning, Path Completion, Session Identification , User Identification, Web Log Mining

Nowadays companies increasingly derive revenue from the creation and sustenance of long-term relationships with their customers. In such an environment, marketing serves the purpose of maximizing customer lifetime value (CLV) and customer equity, which is the sum of the lifetime values of the company’s customers. A frequently-encountered difficulty for companies wishing to measure customer profitability is that management accounting and reporting systems have tended to reflect product profitability rather than customer profitability. But in spite of these difficulties, Companies looking for methods to know how calculate their customers's CLV. In this paper, we used K-Mean clustering approach to determine customers's CLV and segment them based on recency, frequency and monetary (RFM) measures. We also used Discriminant analysis to approve clustering results. Data required applying this method gathered from one branch of an Iranian private bank which is established newly. Finally, in terms of this segmentation, we proposed customer retention strategies for treating with the bank customers.

In this research, a technique for `Credit Card Fraud Detection' is developed. As fraudsters are increasing day by day. And fallacious transactions are done by the credit card and there are various types of fraud. So to solve this problem combination of technique is used like Genetic Algorithm, Behavior Based Technique and Hidden Markov Model. By this transaction is tested individually and whatever suits the best is further proceeded. And the foremost goal is to detect fraud by filtering the above techniques to get better result.

In this paper, various enhanced sales forecast methodologies and models for the automobile market are presented. The methods used deliver highly accurate predictions while maintaining the ability to explain the underlying model at the same time. The representation of the economic training data is discussed, as well as its effects on the newly registered automobiles to be predicted. The methodology mainly consists of time series analysis and classical Data Mining algorithms, whereas the data is composed of absolute and/or relative market-specific exogenous parameters on a yearly, quarterly, or monthly base. It can be concluded that the monthly forecasts were especially improved by this enhanced methodology using absolute, normalized exogenous parameters. Decision Trees are considered as the most suitable method in this case, being both accurate and explicable. The German and the US-American automobile market are presented for the evaluation of the forecast models.

Attempting to successfully and accurately predict the financial market has long attracted the interests and attention of economists, bankers, mathematicians and scientists alike. The financial markets form the bedrock of any economy. There are a large number of factors and parameters that influence the direction, volume, price and flow of traded stocks. This coupled with the markets’ vulnerability to external and non-finance related factors and the resulting intrinsic volatility makes the development of a robust and accurate financial market prediction model an interesting research and engineering problem. In an attempt to solve this engineering problem, the authors of this paper present a rough set theory based predictive model for the financial markets. Rough set theory has, as its base, imperfect data analysis and approximation. The theory is used to extract a set of reducts and a set of trading rules based on trading data of the Johannesburg Stock Exchange (JSE) for the period 1 April 2006 to 1 April 2011. To increase the efficiency of the model four of dicretization algorithms were used on the data set, namely (Equal Frequency Binning (EFB), Boolean Reasoning, Entropy and the Naïve Algorithm. The EFB algorithm gives the least number of rules and highest accuracy. Next, the reducts are extracted using the Genetic Algorithm and finally the set of dependency rules are generated from the set of reducts. A rough set confusion matrix is used to assess the accuracy of the model. The model gave a prediction accuracy of 80.4% using the Standard Voting classifier. Keywords-rough set theory; financial market modelling, neural networks, discretization, classification. INTRODUCTION Around the world, trading in the stock market has gained enormous popularity as a means through which one can reap huge profits. Attempting to successfully and accurately predict the financial market has long attracted the interests and attention of economists, bankers, mathematicians and scientists alike. Thus, stock price movement prediction has long been a cherished desire of investors, speculators and industries [1]. For a long time statistical techniques such as Bayesian models, regression and some econometric techniques have dominated research activities in prediction [2]. The primary approach to financial forecasting has been the identification of a stock price trend and continuation of the investment strategy until evidence suggests that the trend has reversed [3]. One of the biggest problems with the use of regression methods is that they fail to give satisfactory forecasting results for some series’ because of their linear structure and other inherent limitations [8, 9]. However the emergence of computational intelligence techniques as a viable alternative to the “traditional” statistical models that have dominated this area since the 1930’s [2,3] has given impetus to the increasing usage of these techniques in fields such as economics and finance [3]. Apart from these, there have been many other successful applications of intelligent systems to decision support and complex automation tasks [4 6]. Since the year of its inception in 1982, rough set theory has been extensively used as an effective data mining and knowledge discovery technique in numerous applications in the finance, investment and banking fields [4, 7, 11, 12, 20]. Data mining is a discipline in computational intelligence that deals with knowledge discovery, data analysis, and full and semi-autonomous decision making [7]. It entails the analysis of data sets such that unsuspected relationships among data objects are found. Predictive modeling is the practice of deriving future inferences based on these relationships. The financial markets form the bedrock of any economy. There are a large number of factors and parameters that influence the direction, volume, price and flow of traded stocks. This coupled with the markets’ vulnerability to external and nonfinance related factors and the resulting intrinsic volatility makes the development of a robust and accurate financial market prediction model an interesting research and engineering problem. This paper presents a generic stock price prediction model based on rough set theory. The model is derived on data from the daily movements of the Johannesburg Stock Exchange’s All Share Index. The data used was collected over a five year period from 1 April 2006 to 1 April 2011. The methodology used in this paper is as follows: data preprocessing, data splitting, data discreetization, redundant attribute elimination, reduct generation, rule generation and prediction. 71 The rest of this paper is organized as follows: Section II covers the theoretical foundations of rough set theory, Section III gives the design of the proposed prediction model while an analysis of the results and conclusions are presented in Sections IV and V respectively. THEORETICAL FOUNDATIONS OF ROUGH SETS Rough set theory (RST) was introduced by Pawlak in 1982. The theory can be regarded as a mathematical tool used for imperfect data analysis [10]. Thus RST has proved useful in applications spanning the engineering, financial and decision support domains to mention but a few. It is based on the assumption that “with every object in the universe of discourse, some information (data or knowledge) is associated” [10]. In practical applications, the “universe of discourse” described in [10] is usually a table called the decision table in which the rows are objects or data elements and the columns are attributes and the entries are called the attribute values [3]. The objects or data elements described by the same attributes are said to be indiscernible (indistinguishable) by the attribute set. Any set of indiscernible data elements forms a granule or atom of knowledge about the entire “universe of discourse” (information system framework) [10]. A union of these elementary sets (granules) is said to be a precise or crisp set, other-wise the set is said to be rough [1,2,7,10]. Every rough set will have boundary cases i.e data objects which cannot certainly be classified as belonging to the set or its complement when using the available information [10]. Associated with every rough set is a pair of sets called the lower and upper approximation of the rough set. The lower approximation consists of those objects which one can definitively say belong to the target set. The upper approximation consists of those objects which possibly belong to the target set. The difference between the two sets is the boundary region. The decision rule derived specifies an outcome based on certain conditions. Where the derived rule uniquely identifies outcomes based on some conditions the rule is said to be certain else it is uncertain. Every decision rule has a pair of probabilities associated with it, the certainty and coverage coefficients [3]. These conditional probabilities also satisfy Bayes’ theorem [7,10]. The certainty coefficient is the conditional probability that an object that belongs to the decision class outlined by the rule given that it satisfies the conditions of the rule. The coverage coefficient on other hand expresses the conditional probability of reasons given some decision [10]. Clearly RST can be seen to overlap with many other theories in the realm of imperfect knowledge analysis such as evidence theory, Bayesian inference, fuzzy sets etc [1, 3, 4, 10, 11, 12]. To define rough sets mathematically, we begin by defining an information system S = (U,A), where U and A are finite and non-empty sets that represent the data objects and attributes respectively. Every attribute has a set of possible values Va. Va is called the domain of a. A subset of A say B will determine a binary relation I(B) on U, which is called the indiscerniblity relation. The relation is defined as follows: ( ) ( ) if and only if a(x) = a(y) for every a in B, where a(x) denotes the value of attribute a for data object x [10]. I(B) is an equivalence relation. All equivalence classes of I(B) as U/I(B). An equivalence class of I(B) containing x is denoted as B(x). If (x,y) belong to I(B) they are said to be indiscernible with respect to B. All equivalence classes of the indescernibility relation, I(B), are referred to as B-granules or B-elementary sets [10]. In the information system defined above, we define as in [10]: (1) And, (2) We now define the two operators assigned to every (1) two sets called the upper and lower approximation of X. The two sets are defined as follows [10]: ( ) ⋃ { ( ) ( ) } (3) And, ( ) ⋃ { ( ) ( ) } (4) Thus, the lower approximation is the union of all Belementary sets that are included in the target set, whilst the upper approximation is the union of all B-elementary sets that have a non-empty intersection with the target set. The difference between the two sets is called the boundary of region of X.

Business analytics has evolved from being a novelty used by a select few, to an accepted facet of conducting business. Recommender systems form a cri tical component of the business analytics toolkit and, by enabling firms to effectively target custom ers with products and services, are helping alter t h ecommerce landscape. A variety of methods exist for providing recommendations, with collaborative filtering, matrix factorization, and association ru le based methods being the most common. In this pap er, we propose a method to improve the quality of recom mendations made using association rules. This is accomplished by combining rules when possible, and stands apart from existing rule-combination methods in that it is strongly grounded in probabil ity theory. Combining rules requires the identifica t on of the best combination of rules from the many comb inations that might exist, and we use a maximumlikelihood framework to compare alternative combina tions. As it is impractical to apply the maximum likelihood framework directly in real time, we show that this problem can equivalently be represented as a set partitioning problem by translating it into an information theoretic context – the best solution corresponds to the set of rules that leads to the h ighest sum of mutual information associated with th e rules. Through a variety of experiments that evalua te the quality of recommendations made using the proposed approach, we show that (i) a greedy heuris tic u ed to solve the maximum likelihood estimation problem is very effective, providing results compar able to those from using the optimal set partitioni ng solution, (ii) the recommendations made by our appr oach are more accurate than those made by a variety of state-of-the-art benchmarks, including collabora tive filtering and matrix factorization, and (iii) the recommendations can be made in a fraction of a seco nd on a desktop computer, making it practical to us e in real-world applications.

Predicting personalized sequential behavior is a key task for recommender systems. In order to predict user actions such as the next product to purchase, movie to watch, or place to visit, it is essential to take into account both long-term user preferences and sequential patterns (i.e., short-term dynamics). Matrix Factorization and Markov Chain methods have emerged as two separate but powerful paradigms for modeling the two respectively. Combining these ideas has led to unified methods that accommodate long-and short-term dynamics simultaneously by modeling pairwise user-item and item-item interactions. In spite of the success of such methods for tackling dense data, they are challenged by sparsity issues, which are prevalent in real-world datasets. In recent years, similarity-based methods have been proposed for (sequentially-unaware) item recommendation with promising results on sparse datasets. In this paper, we propose to fuse such methods with Markov Chains to make personalized sequential recommendations. We evaluate our method, Fossil, on a variety of large, real-world datasets. We show quantitatively that Fossil outperforms alternative algorithms, especially on sparse datasets, and qualitatively that it captures personalized dynamics and is able to make meaningful recommendations.


Question: A data mining methodology for examining the structure of a web-scale resale marketplace.

This study explores the potential of leveraging historical data from community-based Question Answering (QA) platforms to enhance the resolution of unanswered inquiries. Platforms like Yahoo! Answers and Baidu Zhidao offer a unique space for users to seek detailed and personalized responses from their peers. Yet, a notable portion of questions, approximately 15% in the case of Yahoo! Answers, remain unresolved due to various constraints on answerers' expertise and engagement. Interestingly, our analysis reveals that a quarter of questions in certain categories exhibit recurrence, evident even at the title level, over the span of a year.

To mitigate the issue of unanswered questions, we propose a method to utilize the vast archive of previously resolved questions, openly accessible on the platform. Our focus is on developing a statistical model to predict the likelihood that a new question can be adequately addressed by a previously selected 'best answer'. To build this model, we draw on principles and techniques from query-performance prediction and natural language processing, extracting a comprehensive set of features. The primary hurdle is to ensure the quality of automated responses matches that of the top human answerers.

Our algorithm's effectiveness was assessed both offline using historical data from Yahoo! Answers and online through the deployment of three live answering bots. These bots automatically supply past answers to new questions upon reaching a predetermined confidence threshold. We present the performance metrics of these bots in three active Yahoo! Answers categories, evaluating their success in terms of accuracy, coverage, and user satisfaction. To our knowledge, this study represents the first systematic attempt to automatically address social questions by repurposing high-quality past responses.

Accessing the wealth of structured data available on the Data Web is still a key challenge for lay users. Keyword search is the most convenient way for users to access information (e.g., from data repositories). In this paper we introduce a novel approach for determining the correct resources for user-supplied keyword queries based on a hidden Markov model. In our approach the user-supplied query is modeled as the observed data and the background knowledge is used for parameter estimation. Instead of learning parameter estimation from training data, we leverage the semantic relationships between data items for computing the parameter estimations. In order to maximize accuracy and usability, query segmentation and resource disambiguation are mutually tightly interwoven. First, an initial set of potential segmentations is obtained leveraging the underlying knowledge base; then the final correct set of segments is determined after the most likely resource mapping was computed using a scoring function. While linguistic methods like named entity, multi-word unit recognition and POS-tagging fail in the case of an incomplete sentences (e.g. for keyword-based queries), we will show that our statistical approach is robust with regard to query expression variance. Our experimental results when employing the hidden Markov model for resource identification in keyword queries reveal very promising results.

The problem of mining sequential patterns was recently introduced in [AS95]. We are given a database of sequences, where each sequence is a list of transactions ordered by transaction-time, and each transaction is a set of items. The problem is to discover all sequential patterns with a user-speci ed minimum support, where the support of a pattern is the number of data-sequences that contain the pattern. An example of a sequential pattern is \5% of customers bought `Foundation' and `Ringworld' in one transaction, followed by `Second Foundation' in a later transaction". We generalize the problem as follows. First, we add time constraints that specify a minimum and/or maximum time period between adjacent elements in a pattern. Second, we relax the restriction that the items in an element of a sequential pattern must come from the same transaction, instead allowing the items to be present in a set of transactions whose transaction-times are within a user-speci ed time window. Third, given a user-de ned taxonomy (is-a hierarchy) on items, we allow sequential patterns to include items across all levels of the taxonomy. We present GSP, a new algorithm that discovers these generalized sequential patterns. Empirical evaluation using synthetic and real-life data indicates that GSP is much faster than the AprioriAll algorithm presented in [AS95]. GSP scales linearly with the number of data-sequences, and has very good scale-up properties with respect to the average datasequence size. Also, Department of Computer Science, University of Wisconsin, Madison.

In the massive online worlds of social media, users frequently rely on organizing themselves around specific topics of interest to find and engage with like-minded people. However, navigating these massive worlds and finding topics of specific interest often proves difficult because the worlds are mostly organized haphazardly, leaving users to find relevant interests by word of mouth or using a basic search feature. Here, we report on a method using the backbone of a network to create a map of the primary topics of interest in any social network. To demonstrate the method, we build an interest map for the social news web site reddit and show how such a map could be used to navigate a social media world. Moreover, we analyze the network properties of the reddit social network and find that it has a scale-free, small-world, and modular community structure, much like other online social networks such as Facebook and Twitter. We suggest that the integration of interest maps into popular social media platforms will assist users in organizing themselves into more specific interest groups, which will help alleviate the overcrowding effect often observed in large online communities. Subjects Network Science and Online Social Networks, Visual Analytics

Recommending appropriate product items to the target user is becoming the key to ensure continuous success of E-commerce. Today, many E-commerce systems adopt various recommendation techniques, e.g., Collaborative Filtering (abbreviated as CF)-based technique, to realize product item recommendation. Overall, the present CF recommendation can perform very well, if the target user owns similar friends (user-based CF), or the product items purchased and preferred by target user own one or more similar product items (item-based CF). While due to the sparsity of big rating data in E-commerce, similar friends and similar product items may be both absent from the user-product purchase network, which lead to a big challenge to recommend appropriate product items to the target user. Considering the challenge, we put forward a Structural Balance Theory-based Recommendation (i.e., SBT-Rec) approach. In the concrete, (I) user-based recommendation: we look for target user's “enemy” (i.e., the users having opposite preference with target user); afterwards, we determine target user's “possible friends”, according to “enemy's enemy is a friend” rule of Structural Balance Theory, and recommend the product items preferred by “possible friends” of target user to the target user. (II) likewise, for the product items purchased and preferred by target user, we determine their “possibly similar product items” based on Structural Balance Theory and recommend them to the target user. At last, the feasibility of SBT-Rec is validated, through a set of experiments deployed on MovieLens-1M dataset.

Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed.

Data Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in that field are knowledge intensive and can often benefit from using additional knowledge from various sources. Therefore, many approaches have been proposed in this area that combine Semantic Web data with the data mining and knowledge discovery process. Semantic Web knowledge graphs are a backbone of many information systems that require access to structured knowledge. Such knowledge graphs contain factual knowledge about real word entities and the relations between them, which can be utilized in various natural language processing, information retrieval, and any data mining applications. Following the principles of the Semantic Web, Semantic Web knowledge graphs are publicly available as Linked Open Data. Linked Open Data is an open, interlinked collection of datasets in machine-interpretable form, covering most of the real world domains. In this thesis, we investigate the hypothesis if Semantic Web knowledge graphs can be exploited as background knowledge in different steps of the knowledge discovery process, and different data mining tasks. More precisely, we aim to show that Semantic Web knowledge graphs can be utilized for generating valuable data mining features that can be used in various data mining tasks. Identifying, collecting and integrating useful background knowledge for a given data mining application can be a tedious and time consuming task. Furthermore, most data mining tools require features in propositional form, i.e., binary, nominal or numerical features associated with an instance, while Linked Open Data sources are usually graphs by nature. Therefore, in Part I, we evaluate unsupervised feature generation strategies from types and relations in knowledge graphs, which are used in different data mining tasks, i.e., classification, regression, and outlier detection. As the number of generated features grows rapidly with the number of instances in the dataset, we provide a strategy for feature selection in hierarchical feature space, in order to select only the most informative and most representative features for a given dataset. Furthermore, we provide an end-to-end tool for mining the Web of Linked Data, which provides functionalities for each step of the knowledge discovery process, i.e., linking local data to a Semantic Web knowledge graph, integrating features from multiple knowledge graphs, feature generation and selection, and building machine learning models. However, we show that such feature generation strategies often lead to high dimensional feature vectors even after

Domain-specific search engines are growing in popularity because they offer increased accuracy and extra functionality not possible with the general, Web-wide search engines. For example, www.campsearch.com allows complex queries by age-group, size, location and cost over .summer camps. Unfortunately these domain-specific search engines are difficult and timeconsuming to maintain. This paper proposes the use of machine learning techniques to greatly automate the creation and maintenance of domain-specific search engines. We describe new research in reinforcement learning, information extraction and text classification that enables efficient spidering, identifying informative text segments, and populating topic hierarchies. Using these techniques, we have built a demonstration system: a search engine for computer science research papers. It already contaius over 50,000 papers and is publicly available at ~w. cora.justres earch, com.

This paper presents a data mining methodology to analyze the careers of University graduated students. We present different approaches based on clustering and sequential patterns techniques in order to identify strategies for improving the performance of students and the scheduling of exams. We introduce an ideal career as the career of an ideal student which has taken each examination just after the end of the corresponding course, without delays. We then compare the career of a generic student with the ideal one by using the different techniques just introduced. Finally, we apply the methodology to a real case study and interpret the results which underline that the more students follow the order given by the ideal career the more they get good performance in terms of graduation time and final grade. 2015 Elsevier Ltd. All rights reserved.

This paper addresses the role of marketing in hypermedia computer-mediated environments (CMEs). Our approach considers hypermedia CMEs to be large-scale (i.e. national or global) networked environments, of which the World Wide Web on the Internet is the first and current global implementation. We introduce marketers to this revolutionary new medium, and propose two structural models of consumer behavior in a CME. Then we examine the set of consequent testable research propositions and marketing implications that flow from the models. Marketing in Hypermedia Computer-Mediated Environments: Conceptual Foundations 1) Introduction Firms communicate with their customers through various media. Traditionally, these media follow a passive one-to-many communication model whereby a firm reaches many current and potential customers, segmented or not, through marketing efforts that allow only limited forms of feedback on the part of the customer. For several years now, a revolution has been developing that is dramatically altering this traditional view of advertising and communication media. This revolution is the Internet, the massive global network of interconnected packet-switched computer networks, and as a new marketing medium, has the potential to radically change the way firms do business with their customers. The Internet operationalizes a model of distributed computing that facilitates interactive multimedia many-to-many communication. As such, the Internet supports discussion groups (e.g. USENET news and moderated and unmoderated mailing lists), multi-player games and communications systems (e.g. MUDs, irc, chat, MUSEs), file transfer, electronic mail, and global information access and retrieval systems (e.g. archie, gopher, and the World Wide Web). The business implications of this model "[where] the engine of democratization sitting on so many desktops is already out of control, is already creating new players in a new game" (Carroll 1994), will be played out in as yet unknown ways for years to come. This paper is concerned with the marketing implications of commercializing hypermedia computer-mediated environments (CMEs), of which the World Wide Web (Berners-Lee et. al. 1992, 1993) on the Internet is the first and current networked global implementation. While we provide a formal definition subsequently, at this point we informally define a hypermedia CME as a distributed computer network used to access and provide hypermedia content (i.e., multimedia content connected across the network with hypertext links). Though other CMEs are relevant to marketers, including private bulletin board systems (Bunch 1994); public conferencing systems such as the WELL (Figallo 1993; Rheingold 1992, 1993) and ECHO; and commercial online services such as America On-Line, Prodigy, and CompuServe, we restrict our current focus to marketing activities in hypermedia CMEs accessible via the "Web" on the Internet. The Internet is an important focus for marketers because consumers and firms are conducting business on the Internet in proportions that dwarf the commercial provider base of the other CMEs combined. There are over 21,700 commercial Internet addressess (Verity and Hof 1994), and an increasing percentage of these commercial addresses are providing Web services. As of December 28, 1994, 1465 firms were listed in Open Market’s (1994) directory of "Commercial Services on the Net," and there were 6370 entries in the "Business/Corporations" directory of the Yahoo Guide to WWW (Filo and Yang 1994). The central thesis driving this research is that hypermedia CMEs, such as but not limited to the World Wide Web on the Internet, require the development and application of new marketing concepts and models. This is because hypermedia CMEs possess unique characteristics, including machine-interactivity, telepresence, hypermedia, and network navigation, which distinguish them from traditional media and some interactive multimedia, on which conventional concepts and models are based. Hoffman & Novak (1995), "Marketing in Hypermedia CMEs: Conceptual Foundations" page 1

Most real-world data is heterogeneous and richly interconnected. Examples include the Web, hypertext, bibliometric data and social networks. In contrast, most statistical learning methods work with “flat” data representations, forcing us to convert our data into a form that loses much of the link structure. The recently introduced framework of probabilistic relational models(PRMs) embraces the object-relational nature of structured data by capturing probabilistic interactions between attributes of related entities. In this paper, we extend this framework by modeling interactions between the attributes and the link structure itself. An advantage of our approach is a unified generative model for both content and relational structure. We propose two mechanisms for representing a probabilistic distribution over link structures: reference uncertaintyandexistence uncertainty . We describe the appropriate conditions for using each model and present learning algorithms for each. We present experimental results showing that the learned models can be used to predict link structure and, moreover, the observed link structure can be used to provide better predictions for the attributes in the model.