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Best Clustering Configuration Metrics: Towards Multiagent Based Clustering

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Advanced Data Mining and Applications (ADMA 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6440))

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Abstract

Multi-Agent Clustering (MAC) requires a mechanism for identifying the most appropriate cluster configuration. This paper reports on experiments conducted with respect to a number of validation metrics to identify the most effective metric with respect to this context. This paper also describes a process whereby such metrics can be used to determine the optimum parameters typically required by clustering algorithms, and a process for incorporating this into a MAC framework to generate best cluster configurations with minimum input from end users.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Liverpool, UK

    Santhana Chaimontree, Katie Atkinson & Frans Coenen

Authors
  1. Santhana Chaimontree
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  2. Katie Atkinson
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  3. Frans Coenen
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Editor information

Editors and Affiliations

  1. Faculty of Engineering and Information Technology, University of Technology Sydney, 2007, Sydney, NSW, Australia

    Longbing Cao

  2. College of Computer Science, Chongqing University, 400030, Chongqing, China

    Yong Feng

  3. College of Computer Science, Chongqing University , 400030, Chongqing, China

    Jiang Zhong

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Chaimontree, S., Atkinson, K., Coenen, F. (2010). Best Clustering Configuration Metrics: Towards Multiagent Based Clustering. In: Cao, L., Feng, Y., Zhong, J. (eds) Advanced Data Mining and Applications. ADMA 2010. Lecture Notes in Computer Science(), vol 6440. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17316-5_5

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  • DOI: https://doi.org/10.1007/978-3-642-17316-5_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17315-8

  • Online ISBN: 978-3-642-17316-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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