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BOOK: DATA CLASSIFICATION: ALGORITHMS AND APPLICATIONS
Ed. Charu Aggarwal, CRC Press, 2014
TABLE OF CONTENTS and INTRODUCTION:
http://www.charuaggarwal.net/classbook.pdf
Comprehensive Coverage in the form of surveys on the entire area of
Data 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.
Features
- Integrates different perspectives from the pattern recognition,
database, data mining, and machine learning communities
- Presents an overview of the core methods in data classification
- Covers recent problem domains, such as graphs and social networks
- Discusses advanced methods for enhancing the quality of the underlying
classification results
The table of contents and the introduction may be found at
http://www.charuaggarwal.net/classbook.pdf