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Encyclopedia of Machine Learning and Data Mining pp 1–5Cite as

Clustering from Data Streams

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Abstract

Clustering is one of the most popular data mining techniques. In this article, we review the relevant methods and algorithms for designing cluster algorithms under the data streams computational model, and discuss research directions in tracking evolving clusters.

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Recommended Reading

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Author information

Authors and Affiliations

  1. University of Porto, Porto, Portugal

    João Gama

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  1. João Gama
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Correspondence to João Gama .

Editor information

Editors and Affiliations

  1. Engineering (CSE), University of New South Wales School of Computer Science &, Sydney, New South Wales, Australia

    Claude Sammut

  2. Software Engineering, Monash University School of Computer Science &, Melbourne, Victoria, Australia

    Geoffrey I. Webb

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© 2016 Springer Science+Business Media New York

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Gama, J. (2016). Clustering from Data Streams. In: Sammut, C., Webb, G. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7502-7_41-1

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  • DOI: https://doi.org/10.1007/978-1-4899-7502-7_41-1

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  • Publisher Name: Springer, Boston, MA

  • Online ISBN: 978-1-4899-7502-7

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

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Chapter history

  1. Latest

    Clustering from Data Streams
    Published:
    06 April 2024

    DOI: https://doi.org/10.1007/978-1-4899-7502-7_41-2

  2. Original

    Clustering from Data Streams
    Published:
    28 July 2016

    DOI: https://doi.org/10.1007/978-1-4899-7502-7_41-1

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