Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Classification in Streams

  • Charu C. AggarwalEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_52


Knowledge discovery in streams; Learning in streams


The classification problem is a well defined problem in the data mining domain, in which a training data set is supplied, which contains several feature attributes, and a special attribute known as the class attribute. The class attribute is specified in the training data, which is used to model the relationship between the feature attributes and the class attribute. This model is used in order to predict the unknown class label value for the test instance.

A data stream is defined as a large volume of continuously incoming data. The classification problem has traditionally been defined on a static training or test data set, but in the stream scenario, either the training or test data may be in the form of a stream.

Historical Background

The problem of classification has been studied so widely in the classification literature, that a single source for the problem cannot be identified. Most likely, the problem was...

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Recommended Reading

  1. 1.
    Aggarwal CC, editor. Data streams: models and algorithms. Berlin/Heidelberg/New York: Springer; 2007.zbMATHGoogle Scholar
  2. 2.
    Domingos P, Hulten G. Mining high speed data streams. In: Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2000. p. 71–80.Google Scholar
  3. 3.
    James M. Classification algorithms. New York: Wiley; 1985.zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.IBM T. J. Watson Research CenterYorktown HeightsUSA

Section editors and affiliations

  • Divesh Srivastava
    • 1
  1. 1.AT&T Labs - ResearchAT&TBedminsterUSA