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aggarwal charu c. (curatore) - data classification

Data Classification Algorithms and Applications

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Lingua: Inglese
Pubblicazione: 06/2014
Edizione: 1° edizione


While classification is one of the oldest problems in data mining and machine learning, the last 15 years have seen an unusual explosion of work in this area, particularly from the perspective of scalability, and different domains of data, such as biological data, text data, and social networks. In addition, new areas, such as streams and uncertain data, have also been extensively explored. This book will focus on these new developments, and will explore a broader perspective of classification, by integrating different perspectives from the database, data mining, and machine learning communities.

Note Editore

Comprehensive Coverage of the Entire Area of ClassificationResearch 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.


An Introduction to Data Classification Charu C. Aggarwal Feature Selection for Classification: A Review Jiliang Tang, Salem Alelyani, and Huan Liu Probabilistic Models for Classification Hongbo Deng, Yizhou Sun, Yi Chang, and Jiawei Han Decision Trees: Theory and Algorithms Victor E. Lee, Lin Liu, and Ruoming Jin Rule-Based Classification Xiao-Li Li and Bing Liu Instance-Based Learning: A Survey Charu C. Aggarwal Support Vector Machines Po-Wei Wang and Chih-Jen Lin Neural Networks: A Review Alain Biem A Survey of Stream Classification Algorithms Charu C. Aggarwal Big Data Classification Hanghang Tong Text Classification Charu C. Aggarwal and ChengXiang Zhai Multimedia Classification Shiyu Chang, Wei Han, Xianming Liu, Ning Xu, Pooya Khorrami, and Thomas S. Huang Time Series Data Classification Dimitrios Kotsakos and Dimitrios Gunopulos Discrete Sequence Classification Mohammad Al Hasan Collective Classification of Network Data Ben London and Lise Getoor Uncertain Data Classification Reynold Cheng, Yixiang Fang, and Matthias Renz Rare Class Learning Charu C. Aggarwal Distance Metric Learning for Data Classification Fei Wang Ensemble Learning Yaliang Li, Jing Gao, Qi Li, and Wei Fan Semi-Supervised Learning Kaushik Sinha Transfer Learning Sinno Jialin Pan Active Learning: A Survey Charu C. Aggarwal, Xiangnan Kong, Quanquan Gu, Jiawei Han, and Philip S. Yu Visual Classification Giorgio Maria Di Nunzio Evaluation of Classification Methods Nele Verbiest, Karel Vermeulen, and Ankur Teredesai Educational and Software Resources for Data Classification Charu C. Aggarwal Index


Charu C. Aggarwal is a research scientist at the IBM T.J. Watson Research Center. A fellow of the IEEE and the ACM, he is the author/editor of ten books, an associate editor of several journals, and the vice-president of the SIAM Activity Group on Data Mining. Dr. Aggarwal has published over 200 papers, has applied for or been granted over 80 patents, and has received numerous honors, including the IBM Outstanding Technical Achievement Award and EDBT 2014 Test of Time Award. His research interests include performance analysis, databases, and data mining. He earned a Ph.D. from the Massachusetts Institute of Technology.

Altre Informazioni



Condizione: Nuovo
Collana: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
Dimensioni: 10 x 7 in Ø 3.62 lb
Formato: Copertina rigida
Illustration Notes:84 b/w images and 34 tables
Pagine Arabe: 708

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