Book: Data Classification: Algorithms and Applications

Learn a wide variety of data classification techniques and their methods, domains, and variations in this comprehensive survey of the area of data classification.

Data Classification: Algorithms and Applications Book: Data Classification: Algorithms and Applications

Ed. Charu Aggarwal, CRC Press, 2014
Content: 707 Pages | 84 Illustrations


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.


  • 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