Comprehensive Coverage of the Entire Area of Classification Research on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, Data Classification: Algorithms and Applications explores the underlying algorithms of classification as well as applications of classification in a variety of problem domains, including text, multimedia, social network, and biological data. This comprehensive book focuses on three primary aspects of data classification: Methods-The book first describes common techniques used for classification, including probabilistic methods, decision trees, rule-based methods, instance-based methods, support vector machine methods, and neural networks. Domains-The book then examines specific methods used for data domains such as multimedia, text, time-series, network, discrete sequence, and uncertain data. It also covers large data sets and data streams due to the recent importance of the big data paradigm. Variations-The book concludes with insight on variations of the classification process. It discusses ensembles, rare-class learning, distance function learning, active learning, visual learning, transfer learning, and semi-supervised learning as well as evaluation aspects of classifiers.
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Reviewer: Michael Goldberg
Charu Aggarwal is the editor of this compendium of chapters on data classification for data mining applications. He is a distinguished researcher at the IBM Watson Research Center. He has edited and/or written 14 books on data mining and uncertain data. He also has been on the editorial staff for various IEEE journals, among others, and is a fellow at the three main research societies for mathematical sciences and engineering: ACM, IEEE, and SIAM. In this text, Aggarwal has authored or coauthored seven of the 25 chapters-the introductory chapter 1, instance-based learning using lazy methods (chapter 6), a survey of stream classification algorithms in chapter 9, text classification (chapter 11), rare class detection (chapter 17), a survey on active learning in chapter 22, and the final chapter discussing educational and software resources for data classification. Duda and Hart wrote a fundamental treatise [1] on data and pattern classification, which until recently was considered the classic text. Subsequent editions have equally been important. Wu et al. collected and summarized the top ten most important algorithms in the data mining literature [2]. These ten algorithms are likewise discussed in the first eight chapters of this compendium. Aggarwal takes a three-pronged approach in selecting the chapters for this text. One main focus is on the fundamental methods (chapters 1 through 8). A second focus is that of specific data domains (chapters 9 through 16). The third focus is for advanced applications and variations on classic themes (chapters 17 through 23). The last two chapters are generic and can be useful in tandem with any of the prior chapters. Chapter 24 presents evaluation techniques for classification methods, and chapter 25 considers academic resources for data classification. From a commercial point of view, the most popular techniques are C4.5 (commercially available as C5.0) and classification and regression trees (CART). They are discussed in chapter 4 with case studies presenting their practical application. These approaches are based on a classifier-based system that represents the decision-making process by exploring a tree structure. Utilizing the inherent structure, a necessary set of rules can be constructed. The C4.5 algorithm identifies properties in order to form a rule of the form, "if the object has a certain set of properties Π, then its category must be X ." Questions remain about the robustness of these methods in the presence of error, and whether a hybrid approach would improve these methods by incorporating rules obtained according to other criteria. CART precedes C4.5 and its decision tree is obtained by a recursive partitioning algorithm. The main difference between the two is that CART decisions are binary, whereas C4.5 considers multiple outcomes. Stream classification algorithms are considered to be one of the hottest topics, from a practical perspective, in data classification today. Recent advances in hardware and network technology have enabled large amounts of data and network streams to be handled in a given moment. How to reason and learn about the data from the streams is an open and difficult problem. The source of these streams can be as varied as credit card transactions to voice over Internet protocol (VoIP). Aggarwal surveys the topic in chapter 9, with other authors also adding some insights in their respective chapters (1, 2, 4, and 22). Given the credentials of the editor and the quality of the contributions, it goes without saying that this text is an essential reference for anyone interested in data classification or data mining. Online Computing Reviews Service
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A modified k-nearest neighbour k-NN classifier is proposed for supervised remote sensing classification of hyperspectral data. To compare its performance in terms of classification accuracy and computational cost, k-NN and a back-propagation neural .
In this era of big data, processing large scale data efficiently and accurately has become a challenging problem. Ensemble classification is a type of supervised learning that uses multiple experts to generate the final output. It .
This paper focuses on the specific problem of Big Data classification of network intrusion traffic. It discusses the system challenges presented by the Big Data problems associated with network intrusion prediction. The prediction of a possible .
