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Cluster Validation for High-Dimensional Datasets

  • Minho Kim
  • Hyunjin Yoo
  • R. S. Ramakrishna
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3192)

Abstract

Cluster validation is the process of evaluating performance of cluster-ing algorithms under varying input conditions. This paper presents a new solu-tion to the problem of cluster validation in high-dimensional applications. We examine the applicability of conventional cluster validity indices in evaluating the results of high-dimensional clustering and propose new indices that can be applied to high-dimensional datasets. We also propose an algorithm for auto-matically determining cluster dimension. By utilizing the proposed indices and the algorithm, we can discard the input parameters that PROCLUS needs. Ex-perimental studies show that the proposed cluster validity indices yield better cluster validation performance than is possible with conventional indices.

Keywords

high-dimensional clustering cluster validity index unsupervised learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Minho Kim
    • 1
  • Hyunjin Yoo
    • 1
  • R. S. Ramakrishna
    • 1
  1. 1.Department of Information and Communications GISTGwangjuRepublic of Korea

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