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A Cluster Validity Index for Hard Clustering

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Artificial Intelligence and Soft Computing (ICAISC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7268))

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Abstract

This paper describes a new cluster validity index for the well-separable clusters in data sets. The validity indices are necessary for many clustering algorithms to assign the naturally existing clusters correctly. In the presented method, to determine the optimal number of clusters in data sets, the new cluster validity index has been used. It has been applied to the complete link hierarchical clustering algorithm. The basis to define the new cluster validity index is founding of the large increments of intercluster and intracluster distances, when the clustering algorithm is performed. The maximum value of the index determines the optimal number of clusters in the given set simultaneously. Obtained results confirm very good performances of the proposed approach.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Częstochowa University of Technology, Al. Armii Krajowej 36, 42-200, Częstochowa, Poland

    Artur Starczewski

Authors
  1. Artur Starczewski
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, Computer Science Division, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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© 2012 Springer-Verlag Berlin Heidelberg

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Starczewski, A. (2012). A Cluster Validity Index for Hard Clustering. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29350-4_20

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  • DOI: https://doi.org/10.1007/978-3-642-29350-4_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29349-8

  • Online ISBN: 978-3-642-29350-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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