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International Workshop on Machine Learning and Data Mining in Pattern Recognition

MLDM 2007: Machine Learning and Data Mining in Pattern Recognition pp 174–187Cite as

A Bounded Index for Cluster Validity

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 4571)

Abstract

Clustering is one of the most well known types of unsupervised learning. Evaluating the quality of results and determining the number of clusters in data is an important issue. Most current validity indices only cover a subset of important aspects of clusters. Moreover, these indices are relevant only for data sets containing at least two clusters. In this paper, a new bounded index for cluster validity, called the score function (SF), is introduced. The score function is based on standard cluster properties. Several artificial and real-life data sets are used to evaluate the performance of the score function. The score function is tested against four existing validity indices. The index proposed in this paper is found to be always as good or better than these indices in the case of hyperspheroidal clusters. It is shown to work well on multi-dimensional data sets and is able to accommodate unique and sub-cluster cases.

Keywords

  • clustering
  • cluster validity
  • validity index
  • k-means

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Authors and Affiliations

  1. Ecole Polytechnique Fédérale de Lausanne (EPFL), Station 18, 1015 Lausanne, Switzerland

    Sandro Saitta, Benny Raphael & Ian F. C. Smith

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  1. Sandro Saitta
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  2. Benny Raphael
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  3. Ian F. C. Smith
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    Saitta, S., Raphael, B., Smith, I.F.C. (2007). A Bounded Index for Cluster Validity. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2007. Lecture Notes in Computer Science(), vol 4571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73499-4_14

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    • DOI: https://doi.org/10.1007/978-3-540-73499-4_14

    • Publisher Name: Springer, Berlin, Heidelberg

    • Print ISBN: 978-3-540-73498-7

    • Online ISBN: 978-3-540-73499-4

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