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Data classification: algorithms and applications

Contributor(s): Aggarwal, Charu C [Editor].
Publisher: Boca Raton CRC Press 2015Description: xxvii, 671 p.ISBN: 9781466586741.Subject(s): File organization - Computer science | MathematicsDDC classification: 005.741 Summary: 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. (https://www.crcpress.com/Data-Classification-Algorithms-and-Applications/Aggarwal/p/book/9781466586741)
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Books Vikram Sarabhai Library
Slot 88 (0 Floor, West Wing) Non-fiction 005.741 D2 (Browse shelf) Checked out 16/12/2019 192179

Table of Contents:

1. An introduction to data classification / Charu C. Aggarwal
2. Feature selection for classification : a review / Jiliang Tang, Salem Alelyani, and Huan Liu
3. Probabilistic models for classification / Hongbo Deng, Yizhou Sun, Yi Chang, and Jiawei Han
4. Decision trees : theory and algorithms / Victor E. Lee, Lin Liu, and Ruoming Jin
5. Rule-based classification / Xiao-Li Li and Bing Liu
6. Instance-based learning : a survey / Charu C. Aggarwal
7. Support vector machines / Po-Wei Wang and Chih-Jen Lin
8. Neural networks : a review / Alain Biem
9. A survey of stream classification algorithms / Charu C. Aggarwal
10. Big data classification / Hanghang Tong
11. Text classification / Charu C. Aggarwal and ChengXiang Zhai
12. Multimedia classification / Shiyu Chang, Wei Han, Xianming Liu, Ning Xu, Pooya Khorrami, and Thomas S. Huang
13. Time series data classification / Dimitrios Kotsakos and Dimitrios Gunopulos
14. Discrete sequence classification / Mohammad Al Hasan
15. Collective classification of network data / Ben London and Lise Getoor
16. Uncertain data classification / Reynold Cheng, Yixiang Fang, and Matthias Renz
17. Rare class learning / Charu C. Aggarwal
18. Distance metric learning for data classification / Fei Wang
19. Ensemble learning / Yaliang Li, Jing Gao, Qi Li, and Wei Fan
20. Semi-supervised learning / Kaushik Sinha
21. Transfer learning / Sinno Jialin Pan
22. Active learning : a survey / Charu C. Aggarwal, Xiangnan Kong, Quanquan Gu, Jiawei Han, and Philip S. Yu
23. Visual classification / Giorgio Maria Di Nunzio
24. Evaluation of classification methods / Nele Verbiest, Karel Vermeulen, and Ankur Teredesai
25. Educational and software resources for data classification / Charu C. Aggarwal.



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.

(https://www.crcpress.com/Data-Classification-Algorithms-and-Applications/Aggarwal/p/book/9781466586741)

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