Question: Problems, Difficulties, and Instruments for Clustering Algorithm Applications

This paper introduces a novel cluster separation metric designed to evaluate the similarity of clusters, with an underlying assumption that each cluster's data density decreases as the distance from a cluster's characteristic vector increases. This metric serves a dual purpose: not only does it facilitate the assessment of the suitability of data partitions, but it also enables the comparison of different data division methods in terms of their relative appropriateness. A key advantage of this metric is its independence from the number of clusters being analyzed or the specific partitioning method employed. This versatility makes it a valuable tool for guiding cluster-seeking algorithms, as it can inform the decision-making process in identifying the most suitable data partitioning strategy.

**Question**: A Direct Search Method to solve Economic Dispatch Problem with Valve-Point Effect

**Documents**: A Hybrid Genetic Algorithm for Economic Load Dispatch Problem
In this paper, a hybrid genetic algorithm (HGA) is proposed to solve the economic load dispatch (ELD) problem. The proposed HGA combines the advantages of genetic algorithm (GA) and particle swarm optimization (PSO) to overcome the limitations of conventional GA, such as premature convergence and slow convergence rate. The proposed HGA uses a modified roulette wheel selection mechanism to select the best individuals for crossover and mutation operations. The PSO component of HGA is used to fine-tune the solutions obtained from the GA component, thereby improving the quality of the solutions. The proposed HGA is tested on several benchmark ELD problems and the results are compared with those obtained from other optimization techniques. The results show that the proposed HGA outperforms other optimization techniques in terms of solution quality and computational efficiency.

**Similar Documents**: This work presents an innovative hybrid genetic algorithm (HGA) to tackle the economic load dispatch (ELD) problem, aiming to optimize the distribution of electrical load among generating units. The HGA integrates the strengths of genetic algorithm (GA) and particle swarm optimization (PSO) to address the drawbacks of conventional GA, including premature convergence and sluggish convergence rates. A refined roulette wheel selection strategy is employed to select superior individuals for crossover and mutation operations. The PSO component of the HGA is then utilized to refine the solutions derived from the GA component, enhancing the solution quality. The effectiveness of the HGA is demonstrated through its application to a series of benchmark ELD problems, with the outcomes compared to those achieved by alternative optimization methods. The results substantiate that the proposed HGA surpasses other optimization techniques in terms of both solution quality and computational efficiency.

It is already true that Big Data has drawn huge attention from researchers in information sciences, policy and decision makers in governments and enterprises. As the speed of information growth exceeds Moore’s Law at the beginning of this new century, excessive data is making great troubles to human beings. However, there are so much potential and highly useful values hidden in the huge volume of data. A new scientific paradigm is born as dataintensive scientific discovery (DISD), also known as Big Data problems. A large number of fields and sectors, ranging from economic and business activities to public administration, from national security to scientific researches in many areas, involve with Big Data problems. On the one hand, Big Data is extremely valuable to produce productivity in businesses and evolutionary breakthroughs in scientific disciplines, which give us a lot of opportunities to make great progresses in many fields. There is no doubt that the future competitions in business productivity and technologies will surely converge into the Big Data explorations. On the other hand, Big Data also arises with many challenges, such as difficulties in data capture, data storage, data analysis and data visualization. This paper is aimed to demonstrate a close-up view about Big Data, including Big Data applications, Big Data opportunities and challenges, as well as the state-of-the-art techniques and technologies we currently adopt to deal with the Big Data problems. We also discuss several underlying methodologies to handle the data deluge, for example, granular computing, cloud computing, bio-inspired computing, and quantum computing. 2014 Elsevier Inc. All rights reserved.

Clustering and classification are two important techniques of data mining. Classification is a supervised learning problem of assigning an object to one of several pre-defined categories based upon the attributes of the object. While, clustering is an unsupervised learning problem that group objects based upon distance or similarity. Each group is known as a cluster. In this paper we make use of a large database ‘Fisher’s Iris Dataset’ containing 5 attributes and 150 instances to perform an integration of clustering and classification techniques of data mining. We compared results of simple classification technique (using J48 classifier) with the results of integration of clustering and classification technique, based upon various parameters using WEKA (Waikato Environment for Knowledge Analysis), a Data Mining tool. The results of the experiment show that integration of clustering and classification gives promising results with utmost accuracy rate and robustness even when the data set is containing missing values. KeywordsData Mining; J48; KMEANS; WEKA; Fisher’s Iris dataset;

As with many other sectors the amount of agriculture data based are increasing on a daily basis. However, the application of data mining methods and techniques to discover new insights or knowledge is a relatively a novel research area. In this paper we provide a brief review of a variety of Data Mining techniques that have been applied to model data from or about the agricultural domain. The Data Mining techniques applied on Agricultural data include k-means, bi clustering, k nearest neighbor, Neural Networks (NN) Support Vector Machine (SVM), Naive Bayes Classifier and Fuzzy c-means. As can be seen the appropriateness of data mining techniques is to a certain extent determined by the different types of agricultural data or the problems being addressed. This survey summarize the application of data mining techniques and predictive modeling application in the agriculture field.

Automatic grouping and segmentation of images remains a challenging problem in computer vision. Recently, a number of authors have demonstrated good performance on this task using methods that are based on eigenvectors of the a nity matrix. These approaches are extremely attractive in that they are based on simple eigendecomposition algorithms whose stability is well understood. Nevertheless, the use of eigendecompositions in the context of segmentation is far from well understood. In this paper we give a unied treatment of these algorithms, and show the close connections between them while highlighting their distinguishing features. We then prove results on eigenvectors of block matrices that allow us to analyze the performance of these algorithms in simple grouping settings. Finally, we use our analysis to motivate a variation on the existing methods that combines aspects from di erent eigenvector segmentation algorithms. We illustrate our analysis with results on real and synthetic images. Human perceiving a scene can often easily segment it into coherent segments or groups. There has been a tremendous amount of e ort devoted to achieving the same level of performance in computer vision. In many cases, this is done by associating with each pixel a feature vector (e.g. color, motion, texture, position) and using a clustering or grouping algorithm on these feature vectors. Perhaps the cleanest approach to segmenting points in feature space is based on mixture models in which one assumes the data were generated by multiple processes and estimates the parameters of the processes and the number of components in the mixture. The assignment of points to clusters can then be easily performed by calculating the posterior probability of a point belonging to a cluster. Despite the elegance of this approach, the estimation process leads to a notoriously di cult optimization. The frequently used EM algorithm [3] often converges to a local maximum that depends on the initial conditions.

In this paper, we present an improved k-medoids clustering algorithm based on CF-Tree. The algorithm based on the clustering features of BIRCH algorithm, the concept of k-medoids algorithm has been improved. We preserve all the training sample data in an CF-Tree, then use k-medoids method to cluster the CF in leaf nodes of CF-Tree. Eventually, we can get k clusters from the root of the CF-Tree. This algorithm improves obviously the drawbacks of the k-medoids algorithm, such as the time complexity, scalability on large dataset, and can't find the clusters of sizes different very much and the convex shapes. Experiments show that this algorithm enhances the quality and scalability of clustering.

Temporal clustering refers to the partitioning of a time series into multiple nonoverlapping segments that belong to k temporal clusters, in such a way that segments in the same cluster are more similar to each other than to those in other clusters. Temporal clustering is a fundamental task in many fields, such as computer animation, computer vision, health care, and robotics. The applications of temporal clustering in those areas are diverse, and include human-motion imitation and recognition, emotion analysis, human activity segmentation, automated rehabilitation exercise analysis, and human-computer interaction. However, temporal clustering using a completely unsupervised method may not produce satisfactory results. Similar to regular clustering, temporal clustering also benefits from some expert knowledge that may be available. The type of approach that utilizes a small amount of knowledge to “guide” the clustering process is known as “semi-supervised clustering.” Semi-supervised temporal clustering is a strategy in which extra knowledge, in the form of pairwise constraints, is incorporated into the temporal data to help with the partitioning problem. This thesis proposes a process to adapt and transform two kernel-based methods into semi-supervised temporal clustering methods. The proposed process is exclusive to kernel-based clustering methods, and is based on two concepts. First, it uses the idea of instance-level constraints, in the form of must-link and cannot-link, to supervise the clustering methods. Second, it uses a dynamic-programming method to search for the optimal temporal clusters. The proposed process is applied to two algorithms, aligned cluster analysis (ACA) and spectral clustering. To validate the advantages of the proposed temporal semi-supervised clustering methods, a comparative analysis was performed, using the original versions of the algorithm and another semi-supervised temporal cluster. This evaluation was conducted with both synthetic data and two real-world applications. The first application includes two naturalistic audio-visual human emotion datasets, and the second application focuses on human-motion segmentation. Results show substantial improvements in accuracy, with minimal supervision, compared to unsupervised and other temporal semi-supervised approaches, without compromising time performance.

In this paper, we characterize Google applications, based on a one-month Google trace with over 650k jobs running across over 12000 heterogeneous hosts from a Google data center. On one hand, we carefully compute the valuable statistics about task events and resource utilization for Google applications, based on various types of resources (such as CPU, memory) and execution types (e.g., whether they can run batch tasks or not). Resource utilization per application is observed with an extremely typical Pareto principle. On the other hand, we classify applications via a K-means clustering algorithm with optimized number of sets, based on task events and resource usage. The number of applications in the K-means clustering sets follows a Pareto-similar distribution. We believe our work is very interesting and valuable for the further investigation of Cloud environment.

The exponential growth in the amount of data gathered from various sources has resulted in the need for more efficient algorithms to quickly analyze large datasets. Clustering techniques, like K-Means are useful in analyzing data in a parallel fashion. K-Means largely depends upon a proper initialization to produce optimal results. K-means++ initialization algorithm provides solution based on providing an initial set of centres to the K-Means algorithm. However, its inherent sequential nature makes it suffer from various limitations when applied to large datasets. For instance, it makes k iterations to find k centres. In this paper, we present an algorithm that attempts to overcome the drawbacks of previous algorithms. Our work provides a method to select a good initial seeding in less time, facilitating fast and accurate cluster analysis over large datasets.

To support the systematic improvement of business intelligence (BI) in organizations, we have designed and refined a BI maturity model (BIMM) and a respective measurement instrument (MI) in prior research. In this study, we devise an evaluation strategy, and evaluate the validity of the designed measurement artifact. Through cluster analysis of maturity assessments of 92 organizations, we identify characteristic BI maturity scenarios and representative cases for the relevant scenarios. For evaluating the designed instrument, we compare its results with insights obtained from in-depth interviews in the respective companies. A close match between our model's quantitative maturity assessments and the maturity levels from the qualitative analyses indicates that the MI correctly assesses BI maturity. The applied evaluation approach has the potential to be re-used in other design research studies where the validity of utility claims is often hard to prove.

Research in Machine Learning methods to-date remains centered on technological issues and is mostly application driven. This letter summarizes successful applications of machine learning methods that were presented at the Workshop on Machine Learning in Medical Applications. The goals of the workshop were to foster fundamental and applied research in the application of machine learning methods to medical problem solving and to medical research, to provide a forum for reporting significant results, to determine whether Machine Learning methods are able to underpin the research and development on intelligent systems for medical applications, and to identify those areas where increased research is likely to yield advances. A number of recommendations for a research agenda were produced, including both technical and human-centered issues.


Question: Problems, Difficulties, and Instruments of Clustering Algorithms

Pattern Recognition and Scene Interpretation

This paper delves into the intricacies of pattern classification and scene analysis, exploring the multifaceted challenges and the diverse tools available for tackling them. Pattern classification, a fundamental component in artificial intelligence, involves categorizing data into predefined groups based on shared characteristics. Scene analysis, on the other hand, is a more complex task that requires understanding and interpreting the components and context of an image or a scene. Both tasks are crucial in various applications, including computer vision, robotics, and medical imaging.

The issues in pattern classification and scene analysis are manifold. They range from high-dimensional data, which can lead to the curse of dimensionality, to the presence of noise and the need for robust feature extraction. Moreover, the variability in patterns due to changes in scale, orientation, and lighting conditions poses significant challenges. Addressing these issues necessitates the development of sophisticated algorithms capable of handling large datasets efficiently and accurately.

A variety of tools and techniques have been developed to address the complexities of pattern classification and scene analysis. These include traditional statistical methods such as k-means clustering, hierarchical clustering, and Gaussian mixture models, which are effective for simple datasets. However, for more complex scenarios, advanced methods like support vector machines, neural networks, and deep learning architectures have proven to be more successful. Deep learning, in particular, has revolutionized the field with its ability to automatically learn hierarchical feature representations from raw data.

In the realm of scene analysis, methods like object detection, segmentation, and recognition play crucial roles. Convolutional neural networks (CNNs) have emerged as a powerful tool for these tasks, offering state-of-the-art performance in image classification, object detection, and segmentation. Moreover, the integration of recurrent neural networks (RNNs) and long short-term memory (LSTM) units has enabled the analysis of sequential data, such as videos, further enhancing the capabilities of scene analysis systems.

Despite the progress, challenges persist. The need for large annotated datasets, the computational cost of training complex models, and the interpretability of these models remain significant hurdles. Additionally, the adaptation of these techniques to real-world scenarios, where conditions are often unpredictable, requires further research and development.

In conclusion, the field of pattern classification and scene analysis continues to evolve, driven by the need for more accurate, efficient, and adaptable algorithms. As technology advances, the tools and techniques used in these areas will undoubtedly become more sophisticated, addressing the challenges and issues that currently limit their performance.

Clustering data by identifying a subset of representative examples is important for processing sensory signals and detecting patterns in data. Such "exemplars" can be found by randomly choosing an initial subset of data points and then iteratively refining it, but this works well only if that initial choice is close to a good solution. We devised a method called "affinity propagation," which takes as input measures of similarity between pairs of data points. Real-valued messages are exchanged between data points until a high-quality set of exemplars and corresponding clusters gradually emerges. We used affinity propagation to cluster images of faces, detect genes in microarray data, identify representative sentences in this manuscript, and identify cities that are efficiently accessed by airline travel. Affinity propagation found clusters with much lower error than other methods, and it did so in less than one-hundredth the amount of time.

The clustering techniques are the most important part of the data analysis and k-means is the oldest and popular clustering technique used. The paper discusses the traditional K-means algorithm with advantages and disadvantages of it. It also includes researched on enhanced k-means proposed by various authors and it also includes the techniques to improve traditional K-means for better accuracy and efficiency. There are two area of concern for improving K-means; 1) is to select initial centroids and 2) by assigning data points to nearest cluster by using equations for calculating mean and distance between two data points. The time complexity of the proposed K-means technique will be lesser that then the traditional one with increase in accuracy and efficiency. The main purpose of the article is to proposed techniques to enhance the techniques for deriving initial centroids and the assigning of the data points to its nearest clusters. The clustering technique proposed in this paper is enhancing the accuracy and time complexity but it still needs some further improvements and in future it is also viable to include efficient techniques for selecting value for initial clusters(k). Experimental results show that the improved method can effectively improve the speed of clustering and accuracy, reducing the computational complexity of the k-means. Introduction Clustering is a process of grouping data objects into disjointed clusters so that the data in the same cluster are similar, but data belonging to different cluster differ. A cluster is a collection of data object that are similar to one another are in same cluster and dissimilar to the objects are in other clusters [k-4]. At present the applications of computer technology in increasing rapidly which Unnati R. Raval et al, International Journal of Computer Science and Mobile Computing, Vol.5 Issue.5, May2016, pg. 191-203 © 2016, IJCSMC All Rights Reserved 192 created high volume and high dimensional data sets [10]. These data is stored digitally in electronic media, thus providing potential for the development of automatic data analysis, classification and data retrieval [10]. The clustering is important part of the data analysis which partitioned given dataset in to subset of similar data points in each subset and dissimilar to data from other clusters [1]. The clustering analysis is very useful with increasing in digital data to draw meaningful information or drawing interesting patters from the data sets hence it finds applications in many fields like bioinformatics, pattern recognition, image processing, data mining, marketing and economics etc [4]. There have been many clustering techniques proposed but K-means is one of the oldest and most popular clustering techniques. In this method the number of cluster (k) is predefined prior to analysis and then the selection of the initial centroids will be made randomly and it followed by iterative process of assigning each data point to its nearest centroid. This process will keep repeating until convergence criteria met. However, there are shortcomings of K-means, it is important to proposed techniques that enhance the final result of analysis. This article includes researched on papers [1,2,3,4,5,6] which made some very important improvements towards the accuracy and efficiency of the clustering technique. Basic K-means Algorithm : A centroid-based Clustering technique According to the basic K-mean clustering algorithm, clusters are fully dependent on the selection of the initial clusters centroids. K data elements are selected as initial centers; then distances of all data elements are calculated by Euclidean distance formula. Data elements having less distance to centroids are moved to the appropriate cluster. The process is continued until no more changes occur in clusters[k-1]. This partitioning clustering is most popular and fundamental technique [1]. It is vastly used clustering technique which requires user specified parameters like number of clusters k, cluster initialisation and cluster metric [2]. First it needs to define initial clusters which makes subsets (or groups) of nearest points (from centroid) inside the data set and these subsets (or groups) called clusters [1]. Secondly, it finds means value for each cluster and define new centroid to allocate data points to this new centroid and this iterative process will goes on until centroid [3] does not changes. The simplest algorithm for the traditional K-means [2] is as follows; Input: D = {d1, d2, d3,.......dn} // set of n numbers of data points K // The number of desire Clusters Output: A set of k clusters 1. Select k points as initial centroids. 2. Repeat 3. From K clusters by assigning each data point to its nearest centroid. 4. Recompute the centroid for each cluster until centroid does not change [2]. Unnati R. Raval et al, International Journal of Computer Science and Mobile Computing, Vol.5 Issue.5, May2016, pg. 191-203 © 2016, IJCSMC All Rights Reserved 193 Start

The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. I dedicate this book to my grandchildren. Preface The intent of this book is to describe some recent data mining tools that have proven effective in dealing with data sets which often involve uncertain description or other complexities that cause difficulty for the conventional approaches of logistic regression, neural network models, and decision trees. Among these traditional algorithms, neural network models often have a relative advantage when data is complex. We will discuss methods with simple examples, review applications, and evaluate relative advantages of several contemporary methods. Our intent is to cover the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. We have organized the material into three parts. Part I introduces concepts. Part II contains chapters on a number of different techniques often used in data mining. Part III focuses on business applications of data mining. Not all of these chapters need to be covered, and their sequence could be varied at instructor design. The book will include short vignettes of how specific concepts have been applied in real practice. A series of representative data sets will be generated to demonstrate specific methods and concepts. References to data mining software and sites such as www.kdnuggets.com will be provided. Chapter 1 gives an overview of data mining, and provides a description of the data mining process. An overview of useful business applications is provided. Chapter 2 presents the data mining process in more detail. It demonstrates this process with a typical set of data. Visualization of data through data mining software is addressed. Chapter 3 presents memory-based reasoning methods of data mining. Major real applications are described. Algorithms are demonstrated with prototypical data based on real applications. Chapter 4 discusses association rule methods. Application in the form of market basket analysis is discussed. A real data set is described, and a simplified version used to demonstrate association rule methods. Chapter 5 presents fuzzy data mining approaches. Fuzzy decision tree approaches are described, as well as fuzzy association rule applications. Real data mining applications are described and demonstrated Chapter 6 presents Rough …

In recent years, uncertain data management applications have grown in importance because of the large number of hardware applications which measure data approximately. For example, sensors are typically expected to have considerable noise in their readings because of inaccuracies in data retrieval, transmission, and power failures. In many cases, the estimated error of the underlying data stream is available. This information is very useful for the mining process, since it can be used in order to improve the quality of the underlying results. In this paper we will propose a method for clustering uncertain data streams. We use a very general model of the uncertainty in which we assume that only a few statistical measures of the uncertainty are available. We will show that the use of even modest uncertainty information during the mining process is sufficient to greatly improve the quality of the underlying results. We show that our approach is more effective than a purely deterministic method such as the CluStream approach. We will test the approach on a variety of real and synthetic data sets and illustrate the advantages of the method in terms of effectiveness and efficiency.

Time series clustering has been shown effective in providing useful information in various domains. There seems to be an increased interest in time series clustering as part of the effort in temporal data mining research. To provide an overview, this paper surveys and summarizes previous works that investigated the clustering of time series data in various application domains. The basics of time series clustering are presented, including general-purpose clustering algorithms commonly used in time series clustering studies, the criteria for evaluating the performance of the clustering results, and the measures to determine the similarity/dissimilarity between two time series being compared, either in the forms of raw data, extracted features, or some model parameters. The past researchs are organized into three groups depending upon whether they work directly with the raw data either in the time or frequency domain, indirectly with features extracted from the raw data, or indirectly with models built from the raw data. The uniqueness and limitation of previous research are discussed and several possible topics for future research are identified. Moreover, the areas that time series clustering have been applied to are also summarized, including the sources of data used. It is hoped that this review will serve as the steppingstone for those interested in advancing this area of research. 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Kernel methods are popular in clustering due to their generality and discriminating power. However, we show that many kernel clustering criteria have density biases theoretically explaining some practically significant artifacts empirically observed in the past. For example, we provide conditions and formally prove the density mode isolation bias in kernel K-means for a common class of kernels. We call it Breiman’s bias due to its similarity to the histogram mode isolation previously discovered by Breiman in decision tree learning with Gini impurity. We also extend our analysis to other popular kernel clustering methods, e.g., average/normalized cut or dominant sets, where density biases can take different forms. For example, splitting isolated points by cut-based criteria is essentially the sparsest subset bias, which is the opposite of the density mode bias. Our findings suggest that a principled solution for density biases in kernel clustering should directly address data inhomogeneity. We show that density equalization can be implicitly achieved using either locally adaptive weights or locally adaptive kernels. Moreover, density equalization makes many popular kernel clustering objectives equivalent. Our synthetic and real data experiments illustrate density biases and proposed solutions. We anticipate that theoretical understanding of kernel clustering limitations and their principled solutions will be important for a broad spectrum of data analysis applications across the disciplines.

In most parallel DBSCAN algorithms, neighboring points are assigned to the same data partition for parallel processing to facilitate calculation of the density of the neighbors. This data partitioning scheme causes a few critical problems including load imbalance between data partitions, especially in a skewed data set. To remedy these problems, we propose a cell-based data partitioning scheme, pseudo random partitioning , that randomly distributes small cells rather than the points themselves. It achieves high load balance regardless of data skewness while retaining the data contiguity required for DBSCAN. In addition, we build and broadcast a highly compact summary of the entire data set, which we call a two-level cell dictionary , to supplement random partitions. Then, we develop a novel parallel DBSCAN algorithm, Random Partitioning-DBSCAN (shortly, RP-DBSCAN), that uses pseudo random partitioning together with a two-level cell dictionary. The algorithm simultaneously finds the local clusters to each data partition and then merges these local clusters to obtain global clustering. To validate the merit of our approach, we implement RP-DBSCAN on Spark and conduct extensive experiments using various real-world data sets on 12 Microsoft Azure machines (48 cores). In RP-DBSCAN, data partitioning and cluster merging are very light, and clustering on each split is not dragged out by a specific worker. Therefore, the performance results show that RP-DBSCAN significantly outperforms the state-of-the-art algorithms by up to 180 times.

Finding the most interesting correlations among items is essential for problems in many commercial, medical, and scientific domains. For example, what kinds of items should be recommended with regard to what has been purchased by a customer? How to arrange the store shelf in order to increase sales? How to partition the whole social network into several communities for successful advertising campaigns? Which set of individuals on a social network should we target to convince in order to trigger a large cascade of further adoptions? When conducting correlation analysis, traditional methods have both effectiveness and efficiency problems, which will be addressed in this dissertation. Here, we explore the effectiveness problem in three ways. First, we expand the set of desirable properties and study the property satisfaction for different correlation measures. Second, we study different techniques to adjust original correlation measure, and propose two new correlation measures: the Simplified χ with Continuity Correction and the Simplified χ with Support. Third, we study the upper and lower bounds of different measures and categorize them by the bound differences. Combining with the above three directions, we provide guidelines for users to choose the proper measure according to their situations. With the proper correlation measure, we start to solve the efficiency problem for a large dataset. Here, we propose a fully-correlated itemset (FCI) framework to decouple the correlation measure from the need for efficient search. By wrapping the desired measure in our FCI framework, we take advantage of the desired measure’s superiority in evaluating itemsets, eliminate itemsets with irrelevant items, and achieve good computational performance. In addition, we identify a 1-dimensional monotone property of the upper bound of any good correlation measure, and different 2-dimensional

This paper, investigates how academic performance of students evolves over the years in their studies during a study programme. To determine typical progression patterns over the years, students are described by a 4 tuple (e.g. x1, x2, x3, x4), these being the clusters' mean to which a student belongs to in each year of the degree. For this purpose, two consecutive cohorts have been analyzed using X-means clustering. Interestingly the patterns found in both cohorts show that a substantial number of students stay in the same kind of groups during their studies.

It is challenging to map large spatial flow data due to the problem of occlusion and cluttered display, where hundreds of thousands of flows overlap and intersect each other. Existing flow mapping approaches often aggregate flows using predetermined high-level geographic units (e.g. states) or bundling partial flow lines that are close in space, both of which cause a significant loss or distortion of information and may miss major patterns. In this research, we developed a flow clustering method that extracts clusters of similar flows to avoid the cluttering problem, reveal abstracted flow patterns, and meanwhile preserves data resolution as much as possible. Specifically, our method extends the traditional hierarchical clustering method to aggregate and map large flow data. The new method considers both origins and destinations in determining the similarity of two flows, which ensures that a flow cluster represents flows from similar origins to similar destinations and thus minimizes information loss during aggregation. With the spatial index and search algorithm, the new method is scalable to large flow data sets. As a hierarchical method, it generalizes flows to different hierarchical levels and has the potential to support multi-resolution flow mapping. Different distance definitions can be incorporated to adapt to uneven spatial distribution of flows and detect flow clusters of different densities. To assess the quality and fidelity of flow clusters and flow maps, we carry out a case study to analyze a data set of 243,850 taxi trips within an urban area.


Question: Problems, Difficulties, and Resources of Clustering Algorithm Techniques

In this paper, we explore the K-Means-Type Algorithms: A Comprehensive Convergence Theorem and Insight into Local Optimality, focusing on critical aspects and behaviors of the algorithm in cluster analysis. The clustering problem is initially formulated as a nonconvex mathematical optimization problem. A detailed proof of the finite convergence of K-means and similar algorithms is provided, applicable to any metric space. We uncover scenarios under which the algorithm might not converge to a local minimum, highlighting conditions that could lead to such outcomes. Moreover, we demonstrate that under conditions of differentiability, the algorithm converges to a Kuhn-Tucker point, a concept from nonlinear programming indicating points that satisfy certain first-order necessary conditions for optimality. To address the issue of local minimum convergence, we propose a method to achieve a solution that is a local minimum, enhancing the algorithm's effectiveness in practical applications. This analysis provides a deeper understanding of the K-means algorithm and its limitations, offering strategies to overcome common challenges in clustering tasks.

The use of clustering methods for the discovery of cancer subtypes has drawn a great deal of attention in the scientific community. While bioinformaticians have proposed new clustering methods that take advantage of characteristics of the gene expression data, the medical community has a preference for using "classic" clustering methods. There have been no studies thus far performing a large-scale evaluation of different clustering methods in this context. We present the first large-scale analysis of seven different clustering methods and four proximity measures for the analysis of 35 cancer gene expression data sets. Our results reveal that the finite mixture of Gaussians, followed closely by k-means, exhibited the best performance in terms of recovering the true structure of the data sets. These methods also exhibited, on average, the smallest difference between the actual number of classes in the data sets and the best number of clusters as indicated by our validation criteria. Furthermore, hierarchical methods, which have been widely used by the medical community, exhibited a poorer recovery performance than that of the other methods evaluated. Moreover, as a stable basis for the assessment and comparison of different clustering methods for cancer gene expression data, this study provides a common group of data sets (benchmark data sets) to be shared among researchers and used for comparisons with new methods. The data sets analyzed in this study are available at http://algorithmics.molgen.mpg.de/Supplements/CompCancer/ .

We propose a new method, called SimClus, for clustering with lower bound on similarity. Instead of accepting k the number of clusters to find, the alternative similarity-based approach imposes a lower bound on the similarity between an object and its corresponding cluster representative (with one representative per cluster). SimClus achieves a O(log n) approximation bound on the number of clusters, whereas for the best previous algorithm the bound can be as poor as O(n). Experiments on real and synthetic datasets show that our algorithm produces more than 40% fewer representative objects, yet offers the same or better clustering quality. We also propose a dynamic variant of the algorithm, which can be effectively used in an on-line setting.

As the world is going global, people have a lot to choose from when it comes to movies. There are different genres, cultures and languages to choose from in the world of movies. This arises the issue of recommending movies to users in automated systems. So far, a decent number of works has been done in this field. But there is always room for renovation. This paper proposed a machine learning approach to recommend movies to users using K-means clustering algorithm to separate similar users and creating a neural network for each cluster.

Many applications such as news group filtering, text crawling, and document organization require real time clustering and segmentation of text data records. The categorical data stream clustering problem also has a number of applications to the problems of customer segmentation and real time trend analysis. We will present an online approach for clustering massive text and categorical data streams with the use of a statistical summarization methodology. We present results illustrating the effectiveness of the technique.

Clustering methods based on deep neural networks have proven promising for clustering real-world data because of their high representational power. In this paper we propose a systematic taxonomy of clustering methods that utilize deep neural networks. We base our taxonomy on a comprehensive review of recent work and validate the taxonomy in a case study. In this case study, we show that the taxonomy enables researchers and practitioners to systematically create new clustering methods by selectively recombining and replacing distinct aspects of previous methods with the goal of overcoming their individual limitations. The experimental evaluation confirms this and shows that the method created for the case study achieves state-of-the-art clustering quality and surpasses it in some cases.

This paper presents the design of an MPI-based parallel and distributed machine learning platform on large-scale HPC clusters. Researchers and practitioners can implement easily a class of parallelizable machine learning algorithms on the platform, or port quickly an existing non-parallel implementation of a parallelizable algorithm to the platform with only minor modifications. Complicated functions in parallel programming such as scheduling, caching and load balancing are handled automatically by the platform. The platform performance was evaluated in a series of stress tests by using a k-means clustering task on 7,500 hours of speech data (about 2.7 billion 52dimensional feature vectors). Good scalability is demonstrated on an HPC cluster with thousands of CPU cores.

Clustering is an important research topic in data mining that appears in a wide range of unsupervised classification applications. Partitional clustering algorithms such as the k-means algorithm are the most popular for clustering large datasets. The major problem with the k-means algorithm is that it is sensitive to the selection of the initial partitions and it may converge to local optima. In this paper, we present a hybrid two-phase GAI-PSO+k-means data clustering algorithm that performs fast data clustering and can avoid premature convergence to local optima. In the first phase we utilize the new genetically improved particle swarm optimization algorithm (GAI-PSO) which is a population-based heuristic search technique modeled on the hybrid of cultural and social rules derived from the analysis of the swarm intelligence (PSO) and the concepts of natural selection and evolution (GA). The GAI-PSO combines the standard velocity and position update rules of PSOs with the ideas of selection, mutation and crossover from GAs. The GAI-PSO algorithm searches the solution space to find the optimal initial cluster centroids for the next phase. The second phase is a local refining stage utilizing the k-means algorithm which can efficiently converge to the optimal solution. The proposed algorithm combines the ability of the globalized searching of the evolutionary algorithms and the fast convergence of the k-means algorithm and can avoid the drawback of both. The performance of the proposed algorithm is evaluated through several benchmark datasets. The experimental results show that the proposed algorithm is highly forceful and outperforms the previous approaches such as SA, ACO, PSO and k-means for the partitional clustering problem.

Knowledge Discovery and Data Mining are powerful automated data analysis tools and they are predicted to become the most frequently used analytical tools in the near future. The rapid dissemination of these technologies calls for an urgent examination of their social impact. This paper identifies social issues arising from Knowledge Discovery (KD) and Data Mining (DM). An overview of these technologies is presented, followed by a detailed discussion of each issue. The paper's intention is to primarily illustrate the cultural context of each issue and, secondly, to describe the impact of KD and DM in each case. Existing solutions specific to each issue are identified and examined for feasibility and effectiveness, and a solution that provides a suitably contextually sensitive means for gathering and analysing sensitive data is proposed and briefly outlined. The paper concludes with a discussion of topics for further consideration.

Latent Dirichlet Allocation (LDA) is a data clustering algorithm that performs especially well for text documents. In natural-language applications it automatically finds groups of related words (called “latent topics”) and clusters the documents into sets that are about the same “topic”. LDA has also been applied to source code, where the documents are natural source code units such as methods or classes, and the words are the keywords, operators, and programmer-defined names in the code. The problem of determining a topic count that most appropriately describes a set of source code documents is an open problem. We address this empirically by constructing clusterings with different numbers of topics for a large number of software systems, and then use a pair of measures based on source code locality and topic model similarity to assess how well the topic structure identifies related source code units. Results suggest that the topic count required can be closely approximated using the number of software code fragments in the system. We extend these results to recommend appropriate topic counts for arbitrary software systems based on an analysis of a set of open source systems.

A wireless sensor network (WSN)consisting of a large number of tiny sensors can be an effective tool for gathering data in diverse kinds of environments. The data collected by each sensor is communicated to the base station, which forwards the data to the end user. Clustering is introduced to WSNs because it has proven to be an effective approach to provide better data aggregation and scalability for large WSNs. Clustering also conserves the limited energy resources of the sensors. This paper synthesises existing clustering algorithms news's and highlights the challenges in clustering.


Question: Problems, Difficulties, and Methods of Clustering Algorithms

This paper delves into the dual challenges faced by system designers in optimizing complex systems, particularly focusing on the selection and fine-tuning of optimization algorithms. Genetic Algorithms (GA), a subset of adaptive search techniques, have proven their versatility in optimizing diverse and intricate systems. The study explores the application of GA to address the second-tier challenge: the calibration of GA's control parameters to enhance efficiency. Through a series of experiments, the effectiveness of GA in identifying optimally configured versions for numerical optimization tasks is demonstrated. The findings are corroborated by applying the optimized GA to an image registration problem, showcasing its capability to tackle both primary and secondary optimization issues within complex system design.

**Question**: An Overview of Swarm Intelligence Algorithms for the Optimization of Network Routing

**Documents**: A Comparative Study of Ant Colony Optimization and Genetic Algorithms for the Traveling Salesman Problem
Ant Colony Optimization (ACO) and Genetic Algorithms (GAs) are two of the most popular metaheuristic algorithms that have been applied to many optimization problems. This paper presents a comparative study of ACO and GAs for solving the Traveling Salesman Problem (TSP). The performance of these algorithms is evaluated by solving a set of benchmark problems and comparing the results. The results show that ACO outperforms GAs for solving the TSP.

**Similar Documents**: This study offers a detailed comparison between two prominent metaheuristic algorithms—Ant Colony Optimization (ACO) and Genetic Algorithms (GAs)—in their application to the Traveling Salesman Problem (TSP). Both algorithms have garnered significant attention for their effectiveness in addressing a myriad of optimization challenges. Through rigorous testing on a selection of benchmark problems, the paper evaluates and contrasts the performance of ACO and GAs in TSP solutions. The findings reveal that ACO exhibits superior performance over GAs when applied to TSP scenarios, demonstrating its enhanced efficiency in solving this particular optimization problem.

We propose a new method for clustering based on finding maximum margin hyperplanes through data. By reformulating the problem in terms of the implied equivalence relation matrix, we can pose the problem as a convex integer program. Although this still yields a difficult computational problem, the hard-clustering constraints can be relaxed to a soft-clustering formulation which can be feasibly solved with a semidefinite program. Since our clustering technique only depends on the data through the kernel matrix, we can easily achieve nonlinear clusterings in the same manner as spectral clustering. Experimental results show that our maximum margin clustering technique often obtains more accurate results than conventional clustering methods. The real benefit of our approach, however, is that it leads naturally to a semi-supervised training method for support vector machines. By maximizing the margin simultaneously on labeled and unlabeled training data, we achieve state of the art performance by using a single, integrated learning principle.

The increasing availability and significance of large-scale protein-protein interaction (PPI) data has resulted in a flurry of research activity to comprehend the organization, processes, and functioning of cells by analyzing these data at network level. Network clustering, that analyzes the topological and functional properties of a PPI network to identify clusters of interacting proteins, has gained significant popularity in the bioinformatics as well as data mining research communities. Many studies since the last decade have shown that clustering PPI networks is an effective approach for identifying functional modules, revealing functions of unknown proteins, etc. In this paper, we examine this issue by classifying, discussing, and comparing a wide ranging approaches proposed by the bioinformatics community to cluster PPI networks. A pervasive desire of this review is to emphasize the uniqueness of the network clustering problem in the context of PPI networks and highlight why generic network clustering algorithms proposed by the data mining community cannot be directly adopted to address this problem effectively. We also review a closely related problem to PPI network clustering, network summarization, which can enable us to make sense out of the information contained in large PPI networks by generating multi-level functional summaries.

A wide variety of distortion functions, such as squared Eucl idean distance, Mahalanobis distance, Itakura-Saito distance and relative entropy, have been use d for clustering. In this paper, we propose and analyze parametric hard and soft clustering algori thms based on a large class of distortion functions known as Bregman divergences. The proposed algor ithms unify centroid-based parametric clustering approaches, such as classical kmeans, the Linde-Buzo-Gray (LBG) algorithm and information-theoretic clustering, which arise by special hoices of the Bregman divergence. The algorithms maintain the simplicity and scalability of the c lassicalkmeans algorithm, while generalizing the method to a large class of clustering loss functi o s. This is achieved by first posing the hard clustering problem in terms of minimizing the loss i n Bregman information, a quantity motivated by rate distortion theory, and then deriving an it erative algorithm that monotonically decreases this loss. In addition, we show that there is a biject ion between regular exponential families and a large class of Bregman divergences, that we call regula r Bregman divergences. This result enables the development of an alternative interpretation o f a efficient EM scheme for learning mixtures of exponential family distributions, and leads to a si mple soft clustering algorithm for regular Bregman divergences. Finally, we discuss the connection be tween rate distortion theory and Bregman clustering and present an information theoretic analys is of Bregman clustering algorithms in terms of a trade-off between compression and loss in Bregman information.

Use of traditional k-mean type algorithm is limited to numeric data. This paper presents a clustering algorithm based on k-mean paradigm that works well for data with mixed numeric and categorical features. We propose new cost function and distance measure based on co-occurrence of values. The measures also take into account the significance of an attribute towards the clustering process. We present a modified description of cluster center to overcome the numeric data only limitation of k-mean algorithm and provide a better characterization of clusters. The performance of this algorithm has been studied on real world data sets. Comparisons with other clustering algorithms illustrate the effectiveness of this approach. 2007 Elsevier B.V. All rights reserved.

Most current network intrusion detection systems employ signature-based methods or data mining-based methods which rely on labelled training data. This training data is typically expensive to produce. Moreover, these methods have difficulty in detecting new types of attack. Using unsupervised anomaly detection techniques, however, the system can be trained with unlabelled data and is capable of detecting previously “unseen” attacks. In this paper, we present a new density-based and grid-based clustering algorithm that is suitable for unsupervised anomaly detection. We evaluated our methods using the 1999 KDD Cup data set. Our evaluation shows that the accuracy of our approach is close to that of existing techniques reported in the literature, and has several advantages in terms of computational complexity.

Clustering spatial data is a well-known problem that has been extensively studied. Grouping similar data in large 2-dimensional spaces to find hidden patterns or meaningful sub-groups has many applications such as satellite imagery, geographic information systems, medical image analysis, marketing, computer visions, etc. Although many methods have been proposed in the literature, very few have considered physical obstacles that may have significant consequences on the effectiveness of the clustering. Taking into account these constraints during the clustering process is costly and the modeling of the constraints is paramount for good performance. In this paper, we investigate the problem of clustering in the presence of constraints such as physical obstacles and introduce a new approach to model these constraints using polygons. We also propose a strategy to prune the search space and reduce the number of polygons to test during clustering. We devise a density-based clustering algorithm, DBCluC, which takes advantage of our constraint modeling to efficiently cluster data objects while considering all physical constraints. The algorithm can detect clusters of arbitrary shape and is insensitive to noise, the input order, and the difficulty of constraints. Its average running complexity is O(NlogN) where N is the number of

Detecting local clustered anomalies is an intricate problem for many existing anomaly detection methods. Distance-based and density-based methods are inherently restricted by their basic assumptions—anomalies are either far from normal points or being sparse. Clustered anomalies are able to avoid detection since they defy these assumptions by being dense and, in many cases, in close proximity to normal instances. In this paper, without using any density or distance measure, we propose a new method called SCiForest to detect clustered anomalies. SCiForest separates clustered anomalies from normal points effectively even when clustered anomalies are very close to normal points. It maintains the ability of existing methods to detect scattered anomalies, and it has superior time and space complexities against existing distance-based and density-based methods.

We propose a new method, called SimClus, for clustering with lower bound on similarity. Instead of accepting k the number of clusters to find, the alternative similarity-based approach imposes a lower bound on the similarity between an object and its corresponding cluster representative (with one representative per cluster). SimClus achieves a O(log n) approximation bound on the number of clusters, whereas for the best previous algorithm the bound can be as poor as O(n). Experiments on real and synthetic datasets show that our algorithm produces more than 40% fewer representative objects, yet offers the same or better clustering quality. We also propose a dynamic variant of the algorithm, which can be effectively used in an on-line setting.

A broad class of algorithms for knowledge discovery in databases (KDD) relies heavily on similarity queries, i.e. range queries or nearest neighbor queries, in multidimensional feature spaces. Many KDD algorithms perform a similarity query for each point stored in the database. This approach causes serious performance degenerations if the considered data set does not fit into main memory. Usual cache strategies such as LRU fail because the locality of KDD algorithms is typically not high enough. In this paper, we propose to replace repeated similarity queries by the similarity join, a database primitive prevalent in multimedia database systems. We present a schema to transform query intensive KDD algorithms into a representation using the similarity join as a basic operation without affecting the correctness of the result of the considered algorithm. In order to perform a comprehensive experimental evaluation of our approach, we apply the proposed transformation to the clustering algorithm DBSCAN and to the hierarchical cluster structure analysis method OPTICS. Our technique allows the application of any similarity join algorithm, which may be based on index structures or not. In our experiments, we use a similarity join algorithm based on a variant of the X-tree. The experiments yield substantial performance improvements of our technique over the original algorithms. The traditional techniques are outperformed by factors of up to 33 for the X-tree and 54 for the R*-tree.

WHAT IS DATA MINING? WHY DATA MINING? NEED FOR HUMAN DIRECTION OF DATA MINING CROSS-INDUSTRY STANDARD PROCESS: CRISP–DM CASE STUDY 1: ANALYZING AUTOMOBILE WARRANTY CLAIMS: EXAMPLE OF THE CRISP–DM INDUSTRY STANDARD PROCESS IN ACTION FALLACIES OF DATA MINING WHAT TASKS CAN DATA MINING ACCOMPLISH? CASE STUDY 2: PREDICTING ABNORMAL STOCK MARKET RETURNS USING NEURAL NETWORKS CASE STUDY 3: MINING ASSOCIATION RULES FROM LEGAL DATABASES CASE STUDY 4: PREDICTING CORPORATE BANKRUPTCIES USING DECISION TREES CASE STUDY 5: PROFILING THE TOURISM MARKET USING k-MEANS CLUSTERING ANALYSIS


Question: Analyzing dimensional inconsistencies in code and ROS messages: An examination of 5.9 million lines of code.

This paper delves into the integration of units-of-measure theory within programming languages, emphasizing their significance akin to how types function in programming. Just as type consistency in programming ensures a layer of error prevention, dimensional and unit consistency in scientific and engineering equations serves as a preliminary validation of their accuracy. A glaring example of the critical importance of this concept is the 1999 NASA Mars Climate Orbiter disaster, where a misinterpretation between newtons (the SI unit for force) and lbf (poundforce, a non-SI unit occasionally used in the US) led to the catastrophic failure of the mission. Despite the post-disaster inquiry's numerous recommendations, there was no mention of potential programming language advancements to prevent such inconsistencies through static analysis or type-checking mechanisms. Throughout history, numerous proposals have been made to augment programming languages with the capability to statically check units-of-measure. While some have attempted to exploit the sophisticated type systems of languages like C++ and Haskell for this purpose, it often comes at the expense of ease-of-use [18, 6]. More recently, Sun's conceptual design for the Fortress programming language included an innovative type system designed to support dimensions and units [2]. This tutorial aims to explore the units-of-measure design within the F# programming language, the underlying mechanics of its type system, and the theoretical underpinnings that ensure units safety. The tutorial is segmented into three distinct sections:

1. **Theoretical Foundations of Units-of-Measure in Programming**: This section provides an in-depth overview of the theoretical basis for integrating units-of-measure into programming languages. It covers the rationale behind the importance of dimensional and unit consistency in scientific calculations and its parallels with type consistency in programming. This part also discusses the historical context and the lessons learned from past failures, such as the Mars Climate Orbiter incident, highlighting the need for enhanced static analysis and type-checking capabilities in programming languages.

2. **Practical Implementation in F#**: Here, the focus shifts to the practical application of units-of-measure theory within the F# programming language. This section explains the specifics of the F# type system, detailing how it supports the static checking of units and dimensions. It covers the design principles and mechanisms employed by F# to ensure units safety, including the integration of units-of-measure into the type system and how this affects the overall programming experience.

3. **Advanced Topics and Future Directions**: This final part of the tutorial delves into more advanced topics related to units-of-measure

Robot systems must achieve high level goals while remaining reactive to contingencies and new opportunities. This typically requires robot systems to coordinate concurrent activities, monitor the environment, and deal with exceptions. We have developed a new language to support such task-level control. The language, TDL, is an extension of C++ that provides syntactic support for task decomposition, synchronization, execution monitoring, and exception handling. A compiler transforms TDL into pure C++ code that utilizes a platform-independent task management library. This paper introduces TDL, describes the task treerepresentation that underlies the language, and presents some aspects of its implementation and use in an autonomous mobile robot.

The paper presents, and discusses the rationale behind, a method for structuring complex computing systems by the use of what we term “recovery blocks”, “conversations” and “fault-tolerant interfaces”. The aim is to facilitate the provision of dependable error detection and recovery facilities which can cope with errors caused by residual design inadequacies, particularly in the system software, rather than merely the occasional malfunctioning of hardware components.

Jimmy Lin, Adam Roegiest, Luchen Tan, Richard McCreadie, Ellen Voorhees, and Fernando Diaz 1 David R. Cheriton School of Computer Science, University of Waterloo, Ontario, Canada 2 School of Computing Science, University of Glasgow, Scotland, the United Kingdom 3 National Institute for Standards and Technology, Maryland, USA 4 Microsoft Research, New York, USA {jimmylin, aroegies, luchen.tan}@uwaterloo.ca richard.mccreadie@glasgow.ac.uk, ellen.voorhees@nist.gov, fdiaz@microsoft.com

N-fold inspection uses traditional inspections of the user requirements document (URD) but replicates the inspection activities using N independent teams. A pilot study was conducted to explore the usefulness of N-fold inspection during requirements analysis. A comparison of N-fold inspection with other development techniques reveals that N-fold inspection is a cost-effective method for finding faults in the URD and may be a valid technique in the development of mission-critical software systems.

During maintenance developers cannot read the entire code of large systems. They need a way to get a quick understanding of source code entities (such as, classes, methods, packages, etc.), so they can efficiently identify and then focus on the ones related to their task at hand. Sometimes reading just a method header or a class name does not tell enough about its purpose and meaning, while reading the entire implementation takes too long. We study a solution which mitigates the two approaches, i.e., short and accurate textual descriptions that illustrate the software entities without having to read the details of the implementation. We create such descriptions using techniques from automatic text summarization. The paper presents a study that investigates the suitability of various such techniques for generating source code summaries. The results indicate that a combination of text summarization techniques is most appropriate for source code summarization and that developers generally agree with the summaries produced.


Question: A study of dimensional inconsistencies in 5.9 million lines of code and ROS messages.

Exploring ROS: An Open-Source Framework for Robotic Systems
This paper presents an in-depth look at ROS (Robot Operating System), an open-source software framework designed for robotic applications. Contrary to its name, ROS does not function as a traditional operating system, managing processes and scheduling tasks; instead, it operates as a communication layer over the host operating systems in a diverse computing cluster. The paper delves into the relationship between ROS and other established robot software platforms, highlighting its unique position in the field. Additionally, it provides a concise review of the application software that leverages ROS, showcasing the framework's versatility and utility in a range of robotic systems.

**Question**: Evaluating the Impact of Software Architecture on the Maintainability of Android Applications

**Documents**: A systematic mapping study on software architecture recovery
This paper presents a systematic mapping study of the literature on software architecture recovery, which is the process of discovering, documenting, and understanding the architecture of a system. The study identifies and categorizes the methods, techniques, and tools used in software architecture recovery, as well as the challenges and limitations encountered in the process.

**Similar Documents**:
An Extensive Review of Techniques and Tools for Software Architecture Recovery
This paper offers an extensive review of the methodologies, techniques, and tools employed in the critical process of software architecture recovery. Unlike the development of a new system, software architecture recovery involves uncovering, documenting, and comprehending the existing architecture of a software system, often in the context of maintenance or evolution. The paper systematically categorizes various approaches used in architecture recovery, along with an analysis of the hurdles and constraints faced by practitioners during this complex task. By synthesizing the findings from numerous studies, this review aims to provide a comprehensive understanding of the current landscape of software architecture recovery practices.

A dependence cluster is a set of mutually inter-dependent program elements. Prior studies have found that large dependence clusters are prevalent in software systems. It has been suggested that dependence clusters have potentially harmful effects on software quality. However, little empirical evidence has been provided to support this claim. The study presented in this paper investigates the relationship between dependence clusters and software quality at the function-level with a focus on effort-aware fault-proneness prediction. The investigation first analyzes whether or not larger dependence clusters tend to be more fault-prone. Second, it investigates whether the proportion of faulty functions inside dependence clusters is significantly different from the proportion of faulty functions outside dependence clusters. Third, it examines whether or not functions inside dependence clusters playing a more important role than others are more fault-prone. Finally, based on two groups of functions (i.e., functions inside and outside dependence clusters), the investigation considers a segmented fault-proneness prediction model. Our experimental results, based on five well-known open-source systems, show that (1) larger dependence clusters tend to be more fault-prone; (2) the proportion of faulty functions inside dependence clusters is significantly larger than the proportion of faulty functions outside dependence clusters; (3) functions inside dependence clusters that play more important roles are more fault-prone; (4) our segmented prediction model can significantly improve the effectiveness of effort-aware fault-proneness prediction in both ranking and classification scenarios. These findings help us better understand how dependence clusters influence software quality.


Question: A study of dimensional inconsistencies in 5.9 million lines of code and ROS messages.

This paper delves into the integration of physical dimensions into programming languages to address the issue of dimensional consistency in scientific and engineering computations. Currently, most programming languages handle numeric values as dimensionless, which can lead to errors in practical applications. The research presented here explores the enhancement of programming languages with physical dimension support through a type system akin to that found in ML, but with an added layer of polymorphic dimension types. A key contribution is an algorithm that automatically infers the most general dimension types, ensuring that the code is dimensionally consistent. The paper also outlines a translation method to convert the enhanced language into an explicitly-typed version, where dimensions are treated as function arguments. This facilitates a straightforward operational semantics defined by an evaluation relation and a set of rules, proving that dimension errors are impossible in well-typed programs. Moreover, the study employs a denotational semantics to explore more abstract language properties, such as invariance under unit of measure changes, drawing parallels with parametricity in the polymorphic lambda calculus. The paper concludes with a summary and an outlook on future research directions in dimension types and related type systems, emphasizing the importance of dimensional consistency in programming for scientific and engineering domains.

The automated and semi-automated analysis of source code has remained a topic of intense research for more than thirty years. During this period, algorithms and techniques for source-code analysis have changed, sometimes dramatically. The abilities of the tools that implement them have also expanded to meet new and diverse challenges. This paper surveys current work on source-code analysis. It also provides a road map for future work over the next five-year period and speculates on the development of source-code analysis applications, techniques, and challenges over the next 10, 20, and 50 years.

Modern robotic systems are composed of many distributed processes sharing a common communications infrastructure. High bandwidth sensor data is often collected on one computer and served to many consumers. It is vital that every device on the network agrees on how time is measured. If not, sensor data may be at best inconsistent and at worst useless. Typical clocks in consumer grade PCs are highly inaccurate and temperature sensitive. We argue that traditional approaches to clock synchronization, such as the use of NTP are inappropriate in the robotics context. We present an extremely efficient algorithm for learning the mapping between distributed clocks, which typically achieves better than millisecond accuracy within just a few seconds. We also give a probabilistic analysis providing an upper-bound error estimate.


Question: A study of dimensional inconsistencies in ROS messages and code: An analysis of 5.9 million lines of code.

This paper explores the landscape of Robotic Development Environments (RDEs), which have become indispensable tools in the robotics field, particularly for crafting architectures for autonomous mobile robots. Despite their significance, a systematic analysis comparing the available RDEs has been lacking. To fill this gap, a detailed list of evaluation criteria tailored to robotics applications is proposed, aimed at highlighting the advantages and limitations of various RDEs. This study also addresses the scarcity of practical assessments of RDEs' usability and impact, which are essential for researchers to make informed decisions about which RDE best suits their requirements and to identify trends for future RDE enhancements. By scrutinizing nine open-source, accessible RDEs for mobile robots, this survey offers a multifaceted evaluation. Initially, a framework is constructed, drawing on prior work on agent systems, to categorize RDE features and capabilities into four broad groups. Following this, a hands-on evaluation of the RDEs' practicality in the design, implementation, and operation of robot architectures is conducted. Additionally, the survey sheds light on the real-world impact of these RDEs by compiling a list of applications and research endeavors where they have been employed, offering concrete illustrations of their utility across different domains. The paper culminates with an exhaustive comparison of the nine RDEs, accompanied by recommendations on how to leverage the survey's findings and a forward-looking discussion on emerging trends in RDE design.


Question: A study of dimensional inconsistencies in 5.9 million lines of code and ROS messages.

This paper delves into an extensive analysis of dimensional inconsistencies encountered within 5.9 million lines of code, particularly focusing on their impact on ROS (Robot Operating System) messages. Unlike conventional studies, this work examines the nuances of code dimensions and how they align—or misalign—with ROS message structures. The investigation uncovers patterns and anomalies in code that can lead to unexpected behavior in robotic systems, highlighting the importance of maintaining dimensional consistency. The study employs a rigorous methodology, involving static code analysis and dynamic testing, to identify and categorize inconsistencies. Key findings reveal prevalent issues such as mismatched data types, incorrect serialization, and dimension mismatches in message passing. Recommendations are provided for developers to enhance code quality and ROS message handling, ensuring robust and reliable robotic applications. This research not only sheds light on the subtle challenges in robotic software development but also offers practical guidelines to mitigate dimensional inconsistencies, thereby contributing to the advancement of ROS and the broader field of robotics.


Question: Taking Risks Under Influence: A Fuzzy-Trace Theory of Emotional Impact in Adolescence.

A Developmental Perspective on Antisocial Behavior: Two Distinct Pathways.
This paper introduces a dual taxonomy to reconcile the apparent contradiction in the study of antisocial behavior, where it exhibits both strong continuity over time and significant fluctuations in prevalence during adolescence. The taxonomy distinguishes between two distinct groups with unique developmental trajectories and underlying causes: one characterized by life-course-persistent antisocial behavior, and the other by adolescence-limited antisocial behavior. The life-course-persistent pathway is marked by a cumulative interaction between early neuropsychological issues and criminogenic environments, leading to a pathological personality. Conversely, the adolescence-limited pathway is attributed to a modern maturity gap, where adolescents temporarily engage in antisocial behavior as a normative response to societal and developmental pressures, rather than a manifestation of a deeper pathology. This dual taxonomy offers a nuanced understanding of antisocial behavior, highlighting the importance of considering developmental stages and individual differences in etiology.


Question: Taking Risks While Influenced: A Fuzzy-Trace Theory of Emotional Impact in Adolescence.

This study delves into the seemingly contradictory correlations between perceived risk and risk-taking behaviors, aiming to resolve the inconsistencies observed in various studies. A sample of 596 adolescents participated in the research, which involved distinct measures of risk perception, varying in cue specificity and response format. In alignment with the predictions of fuzzy-trace theory, measures that emphasized detailed, verbatim recall and quantitative analysis led to positive correlations between perceived risk and engagement in risky behaviors. In this scenario, risk perceptions mirrored the degree to which adolescents participated in risky activities. Conversely, measures that focused on holistic, gist-based assessments of risk resulted in negative correlations; heightened perceptions of risk were linked to reduced risk-taking, suggesting a protective rather than a reflective relationship. Notably, the adherence to straightforward values and principles emerged as the most potent safeguard against risky behaviors. These findings bolster a dual-process understanding of the relationship between risk perception and risk-taking, where the observed associations hinge on whether the cues prompt verbatim or gist processing.


Question: Taking Risks While Influenced: A Fuzzy-Trace Theory perspective on emotion during adolescence.

This paper presents a dual-system perspective to explore the mechanisms that facilitate or disrupt self-control, exemplified through the lens of delayed gratification. Two distinct systems are proposed: the cool, cognitive 'know' system and the hot, emotional 'go' system. The cool system is characterized by cognitive processes, devoid of emotional charge, and is contemplative, adaptable, integrated, and strategic. It serves as the foundation for self-regulation and self-control. Conversely, the hot system is the source of emotional responses, encompassing both fears and passions. It operates impulsively and reflexively, initially guided by innate triggers or stimuli, making it susceptible to classical conditioning and potentially compromising self-control efforts. The equilibrium between these two systems is influenced by stress, developmental stage, and individual self-regulatory dynamics. The interplay between the cool and hot systems offers a framework for interpreting a wide range of findings on self-control accumulated over three decades of research. This dual-system framework not only highlights the complexity of self-control but also underscores the pivotal role of emotional regulation in the context of delayed gratification.

Using prospective data from the Dunedin Multidisciplinary Health and Development Study birth cohort, the authors found that adolescents with low self-esteem had poorer mental and physical health, worse economic prospects, and higher levels of criminal behavior during adulthood, compared with adolescents with high self-esteem. The long-term consequences of self-esteem could not be explained by adolescent depression, gender, or socioeconomic status. Moreover, the findings held when the outcome variables were assessed using objective measures and informant reports; therefore, the findings cannot be explained by shared method variance in self-report data. The findings suggest that low self-esteem during adolescence predicts negative real-world consequences during adulthood.


Question: Taking Risks While Influenced: A Fuzzy-Trace Theory of Emotional Decision-Making in Adolescents.

Adolescent Decision Making: A Dual-Process Perspective on Emotion and Risk.
Adolescence is a period marked by the emergence of risky behaviors, such as substance abuse, reckless driving, and unhealthy habits, which can have long-lasting effects on health and well-being. Addressing these behaviors early can lead to more positive outcomes in adulthood. Programs aimed at mitigating risky adolescent behaviors should be grounded in a comprehensive understanding of what is normative, descriptive, and prescriptive in adolescent decision-making processes. Normatively, decisions should be coherent and lead to positive outcomes. However, the goals of adolescents often prioritize immediate pleasure over long-term benefits. While adolescents are capable of rational decision making under ideal conditions, situational factors can lead to poorer reasoning. Emotional states, peer presence, and unfamiliar situations can exacerbate risk-taking tendencies. The maturation of the brain during adolescence is incomplete, and individual differences in impulsivity and sensation seeking can contribute to risky behaviors that are resistant to standard interventions. Dual-process models, like fuzzy-trace theory, highlight two paths to risk taking: a reasoned and a reactive route. These models help explain why different individuals may engage in risk taking for distinct reasons. Interventions must consider the diverse causes of risk-taking behavior to be effective. Contrary to common beliefs, adolescents do not perceive themselves as invulnerable, and their risk perceptions actually decrease with age. Moreover, they often overestimate risks such as HIV and lung cancer. Some biases in judgment and decision-making increase with age, leading to more "irrational" violations of coherence among adults. Traditional interventions focusing on accurate risk perceptions may be ineffective or counterproductive, as young people already feel vulnerable. Experience is not always a good teacher for children and younger adolescents, as they may learn little from negative outcomes. However, learning from experience improves with age. Novel interventions that discourage adolescents from weighing risks and benefits deliberately may prove more effective. Mature adults are less likely to take risks because they intuitively understand the gists of risky situations and avoid contemplating tradeoffs between risks and benefits.

Affective influences abound in groups. In this article we propose an organizing model for understanding these affective influences and their effects on group life. We begin with individuallevel affective characteristics that members bring to their groups: moods, emotions, sentiments, and emotional intelligence. These affective characteristics then combine to form a group’s affective composition. We discuss explicit and implicit processes through which this affective combination occurs by examining the research on emotional contagion, entrainment, modeling, and the manipulation of affect. We also explore how elements of the affective context, such as organizationwide emotion norms and the group’s particular emotional history, may serve to constrain or amplify group members’ emotions. The outcome, group emotion, results from the combination of the group’s affective composition and the affective context in which the group is behaving. Last, we focus on the important interaction between nonaffective factors and affective factors in group life and suggest a possible agenda for future research. q 2001 Academic Press


Question: Taking Risks Under Influence: A Fuzzy-Trace Theory of Emotion in the Adolescent Years.

This paper explores the multifaceted issue of adolescent sexual risk behavior from a comprehensive, multi-system perspective, addressing the urgent need to identify and understand the factors contributing to early and unsafe sexual activity among teenagers. Given the significant health risks associated with such behaviors, including sexually transmitted diseases (including human immunodeficiency virus) and unintended pregnancies, there has been a concerted effort by researchers to pinpoint the determinants of sexual risk-taking among adolescents. However, previous research has often been constrained by a narrow focus that fails to encompass the full complexity of the adolescent sexual experience. Here, we critically examine the recent literature (1990-1999) on the correlates of adolescent sexual risk-taking, consolidating the findings within a multi-system framework. This approach considers influences from the individual (self), family, and broader social systems. We also highlight methodological challenges that have hindered the field's progress and discuss the benefits of adopting a multi-system perspective for future research. Finally, we explore the practical implications of the existing research for professionals aiming to mitigate sexual risk behavior among adolescents, emphasizing the need for a holistic understanding of the issue.


Question: Adiabatic charging of capacitors using switched capacitor converters for multiple target voltages.

A Comprehensive and Integrated Framework for Switched Capacitor Converter Analysis

An innovative and integrated methodology has been developed for the detailed analysis of losses in Switched Capacitor Converters (SCC), which has been validated through both simulations and experimental results. This analytical framework is distinctive in its comprehensive coverage, encompassing both hard-switched and soft-switched SCC topologies within a unified model. A key feature of this model is its capacity to express losses as a direct function of the currents traversing each flying capacitor. Given that these currents are proportionally linked to the output current, the model extends its applicability seamlessly to SCC configurations featuring multiple capacitors. By providing a detailed understanding of expected losses in SCC systems and the impact of varying operational conditions, such as duty cycle, the model serves as a valuable tool for optimizing SCC systems and their control strategies to meet specific performance criteria. This comprehensive approach enables designers to make informed decisions, enhancing the efficiency and reliability of SCC systems across various applications.

A novel non-isolated three-port converter (NI-TPC) is proposed interfacing one PV port, one bidirectional battery port and one load port. Single stage power conversion between any two of the three ports is achieved. The topology is derived by decoupling the bidirectional power flow path of the conventional structure into two unidirectional ones. Two of the three ports can be tightly regulated to achieve maximum power harvesting for PV or charge control for battery, and maintain the load voltage constant at the same time, while the third port is left flexible to compensate the power imbalance of the converter. Operation states are analyzed. The multi-regulator competition control strategy is presented to achieve autonomous and smooth state switching when the PV input power fluctuates. The analysis is verified by the experimental results.

A new voltage doubler rectified boost-integrated half bridge converter for digital car audio amplifiers is proposed. The proposed converter shows low conduction loss due to low voltage stress of the secondary diodes, no dc magnetizing current for the transformer, and a lack of stored energy in the transformer. Moreover, since the primary MOSFETs are turned-on under zero voltage switching conditions and the secondary diodes are turned-off under zero current switching conditions, the proposed converter has minimized switching losses. In addition, the input filter can be minimized due to a continuous input current, and an output filter does not need an inductor in the proposed converter. Therefore, the proposed converter has the desired features, high efficiency and low profile, for a viable power supplies for digital car audio amplifiers. A 60-W industrial sample of the proposed converter has been implemented for digital car audio amplifiers with a measured efficiency of 88.3% at nominal input voltage.

In this paper, a new low-energy switching technique with low common-mode voltage variation is proposed for successive-approximation analog-to-digital converters (SA-ADCs). In the proposed scheme, not only the switching energy consumed within the first three comparisons is less than zero, but also other comparisons are made with the low-power monotonic method. Therefore, the switching energy of the capacitive array, including the consumed energy during the sampling phase, is reduced by 90.68% compared with the conventional counterpart. Moreover, since the variation of the input common-mode voltage of the employed comparator is only 0.125Vref, where Vref is the reference voltage of the ADC, the required comparator’s performance can be much more relaxed leading to more power saving. Post-layout simulation results of a 10-bit 1-MS/s SA-ADC in a 0.18-μm CMOS technology show a signal-to-noise-and-distortion ratio (SNDR) of 61 dB, a spurious-free dynamic range (SFDR) of 79.8 dB, and an effective number of 9.84 bits. The ADC consumes 35.3 μW with a 1.8-V supply and achieves a Figure-of-Merit (FoM) of 38.5 fJ/conversion-step.

In this paper, we present an ultra-low power (ULP) zero-current-detector (ZCD) circuit by using sub-threshold design method and discontinued operating time to decrease power consumption, which can be widely used in switching inductor converters such as boost, buck or buck-boost converters. The designed ZCD circuit consumes only 40 nA quiescent current to realize nearly 70 dB DC gain and 650 kHz gain-bandwidth (GBW) when it operates. The main comparator of the ZCD circuit has high sensitivity and very low delay time to turn-off the synchronous switches of the DC-DC converter on time. The average quiescent current consumption is less than 1.6 nA because the operating time is only 40 μs under the minimum switching period of 1 ms. Finally, the designed ZCD circuit is used in a boost converter for vibrational energy harvesting applications.

With the die area of modern processors growing larger and larger, the IR drop across the power supply rail due to its parasitic resistance becomes considerable. There is an urgent demand for local power regulation to reduce the IR drop and to enhance transient response. A distributed power-delivery grid (DPDG) is an attractive solution for large area power-supply applications. The dual-loop distributed micro-regulator in [1] achieves a tight regulation and fast response, but suffers from large ripple and high power consumption due to the comparator-based regulator. Digital low-dropout regulators (DLDOs) [2] can be used as local micro-regulators to implement a DPDG, due to their low-voltage operation and process scalability. Adaptive control [3], asynchronous 3D pipeline control [4], analog-assisted tri-loop control [5], and event-driven PI control [6] are proposed to enhance the transient response speed. However, digital LDOs suffer from the intrinsic limitations of large output ripple and narrow current range. This paper presents an on-chip DPDg with cooperative regulation based on an analog-assisted digital LDO (AADLDO), which inherits the merits of low output ripple and sub-LSB current supply ability from the analog control, and the advantage of low supply voltage operation and adaptive fast response from the digital control.

This paper proposes a new bridge modular switched-capacitor-based ac–ac converter, which is intended to replace conventional transformers in commercial and residential applications, especially for microgrids. The main advantages of the proposed converter lie in high efficiency and power factor, bidirectional operation, and capacitor voltage self-balancing ability. Another distinct merit is its symmetric construction, with which the voltage ripple of charge/pump capacitors can be cancelled out, and the output voltage will be smooth with significant ripple reduction. It is also noteworthy that the symmetric construction ensures that the two symmetric parts can work independently under a simplified control strategy. In addition, the modular configuration contributes to convenient voltage extension. Compared with the reported flying-capacitor switched-capacitor ac–ac converter, the proposed converter requires fewer switches and capacitors per unit of voltage conversion, thus suffering lower voltage stress. To demonstrate the performance of the converter, a prototype with 600-W 220-Vrms high-side voltage, 55-Vrms low-side voltage, and a switching frequency of 100 kHz was designed and fabricated. The relevant experimental results are reported herein. The maximum and rated power efficiency obtained were 95.9% and 94.4%, respectively.

A high efficient LLCC-type resonant dc-dc converter is discussed in this paper for a low-power photovoltaic application. Emphasis is put on the different design mechanisms of the resonant tank. At the same time soft switching of the inverter as well as the rectifier bridge are regarded. Concerning the design rules, a new challenge is solved in designing a LLCC-converter with voltage-source output. Instead of the resonant elements, ratios of them, e.g. the ratio of inductances Ls/Lp is considered as design parameters first. Furthermore, the derived design rule for the transformer-inductor device fits directly into the overall LLCC-design. Due to the nature of transformers, i.e. the relation of the inductances Ls/Lp is only a function of geometry, this design parameter is directly considered by geometry. Experimental results demonstrate the high efficiency.

Multi-phase converters have become a topic of great interest due to the high output power capacity and output ripple cancellation effect. They are even more beneficial to nowadays high-frequency fully integrated converters with output capacitor integrated on-chip. As one of the dominant chip area consumers, reducing the size of the on-chip decoupling capacitors directly leads to cost down. It is reported that a 5× capacitor area reduction can be achieved with a four-phase converter compared to a single-phase one [1]. However, the penalty is obvious. Every extra phase comes with an inductor, which is also counted as cost and becomes more dominant with increase in the number of phases.

Designing integrated circuits able to work under low-voltage (LV) low-power (LP) condition is currently undergoing a very considerable boom. Reducing voltage supply and power consumption of integrated circuits is crucial factor since in general it ensures the device reliability, prevents overheating of the circuits and in particular prolongs the operation period for battery powered devices. Recently, non-conventional techniques i.e. bulkdriven (BD), floating-gate (FG) and quasi-floating-gate (QFG) techniques have been proposed as powerful ways to reduce the design complexity and push the voltage supply towards threshold voltage of the MOS transistors (MOST). Therefore, this paper presents the operation principle, the advantages and disadvantages of each of these techniques, enabling circuit designers to choose the proper design technique based on application requirements. As an example of application three operational transconductance amplifiers (OTA) based on these non-conventional techniques are presented, the voltage supply is only ±0.4 V and the power consumption is 23.5 μW. PSpice simulation results using the 0.18 μm CMOS technology from TSMC are included to verify the design functionality and correspondence with theory.

Large control output ripple in ACMC (average current mode control) PWM DC/DC converters exposes challenges in accurate modeling and design. In this paper, superposition models are utilized to perform control design analysis for ACMC PWM DC/DC converters. The ACMC DC/DC PWM converter system is converted to a cascade control structure and a step-by-step control design algorithm is then proposed. The control design algorithms differ from existing, conventional algorithms because they carefully compensate for newly discovered feed forward influence in the model.