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Advances in K-means Clustering pp 155–175Cite as

K-means Based Consensus Clustering

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Abstract

Consensus clustering, also known as cluster ensemble or clustering aggregation, aims to find a single clustering from multi-source basic clusterings on the same group of data objects. It has been widely recognized that consensus clustering has merits in generating better clusterings, finding bizarre clusters, handling noise, outliers and sample variations, and integrating solutions from multiple distributed sources of data or attributes.

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Notes

  1. 1.

    http://www.strehl.com.

  2. 2.

    http://www.mathworks.cn/help/toolbox/stats/kmeans.html.

  3. 3.

    http://glaros.dtc.umn.edu/gkhome/views/cluto/.

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Authors and Affiliations

  1. Department of Information Systems, School of Economics and Management, Beihang University, Beijing, 100191, China

    Junjie Wu

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  1. Junjie Wu
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© 2012 Springer-Verlag Berlin Heidelberg

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Wu, J. (2012). K-means Based Consensus Clustering. In: Advances in K-means Clustering. Springer Theses. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29807-3_7

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  • DOI: https://doi.org/10.1007/978-3-642-29807-3_7

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29806-6

  • Online ISBN: 978-3-642-29807-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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