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Multi-Objective Machine Learning pp 21–47Cite as

Multi-Objective Clustering and Cluster Validation

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 16))

Abstract

This chapter is concerned with unsupervised classification, that is, the analysis of data sets for which no (or very little) training data is available. The main goals in this data-driven type of analysis are the discovery of a data set’s underlying structure, and the identification of groups (or clusters) of homogeneous data items — a process commonly referred to as cluster analysis.

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Author information

Authors and Affiliations

  1. University of Manchester, Manchester

    Julia Handl & Joshua Knowles

Authors
  1. Julia Handl
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  2. Joshua Knowles
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Editor information

Editors and Affiliations

  1. Honda Research Institute Europe GmbH, Carl-Legien-Str. 30, Offenbach, 63073, Germany

    Yaochu Jin Dr.

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© 2006 Springer

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Handl, J., Knowles, J. (2006). Multi-Objective Clustering and Cluster Validation. In: Jin, Y. (eds) Multi-Objective Machine Learning. Studies in Computational Intelligence, vol 16. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-33019-4_2

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  • DOI: https://doi.org/10.1007/3-540-33019-4_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30676-4

  • Online ISBN: 978-3-540-33019-6

  • eBook Packages: EngineeringEngineering (R0)

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