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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2012: Advances in Knowledge Discovery and Data Mining pp 354–365Cite as

Neighborhood-Based Smoothing of External Cluster Validity Measures

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7301))

Abstract

This paper proposes a methodology for introducing a neighborhood relation of clusters to the conventional cluster validity measures using external criteria, that is, class information. The extended measure evaluates the cluster validity together with connectivity of class distribution based on a neighborhood relation of clusters. A weighting function is introduced for smoothing the basic statistics to set-based measures and to pairwise-based measures. Our method can extend any cluster validity measure based on a set or pairwise of data points. In the experiment, we examined the neighbor component of the extended measure and revealed an appropriate neighborhood radius and some properties using synthetic and real-world data.

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Author information

Authors and Affiliations

  1. The Institute of Scientific and Industrial Research (ISIR), Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, Japan

    Ken-ichi Fukui & Masayuki Numao

Authors
  1. Ken-ichi Fukui
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  2. Masayuki Numao
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Michigan State University, 428 S. Shaw Lane, 48824-1226, East Lansing, MI, USA

    Pang-Ning Tan

  2. School of Information Technologies, University of Sydney, 1 Cleveland St., 2006, Sydney, NSW, Australia

    Sanjay Chawla

  3. Faculty of Computing and Informatics, Jalan Multimedia, Multimedia University, 63100, Cyberjaya, Selangor, Malaysia

    Chin Kuan Ho

  4. Department of Computing and Information Systems, The University of Melbourne, 111 Barry Street, 3053, Melbourne, VIC, Australia

    James Bailey

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© 2012 Springer-Verlag Berlin Heidelberg

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Fukui, Ki., Numao, M. (2012). Neighborhood-Based Smoothing of External Cluster Validity Measures. In: Tan, PN., Chawla, S., Ho, C.K., Bailey, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2012. Lecture Notes in Computer Science(), vol 7301. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30217-6_30

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  • DOI: https://doi.org/10.1007/978-3-642-30217-6_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30216-9

  • Online ISBN: 978-3-642-30217-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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