A new cluster validity measure for clusters with different densities

Chien Hsing Chou, Mu Chun Su, Eugene Lai

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

18 Scopus citations

Abstract

Many validity measures have been proposed for evaluating clustering results. Most of these popular validity measures do not work well for clusters with different densities and/or sizes. They usually have a tendency of ignoring clusters with low densities. In this paper, we propose a new validity measure, which can deal with this situation. The performance evaluation of the validity measure compares favorably to that of several validity functions and shows the effectiveness.

Original languageEnglish
Title of host publicationProceedings of the IASTED International Conference on Intelligent Systems and Control
EditorsM.H. Hamza, M.H. Hamza
Pages276-281
Number of pages6
StatePublished - 2003
EventProceedings of the IASTED International Conference on Intelligent Systems and Control - Salzburg, Austria
Duration: 25 Jun 200327 Jun 2003

Publication series

NameProceedings of the IASTED International Conference on Intelligent Systems and Control

Conference

ConferenceProceedings of the IASTED International Conference on Intelligent Systems and Control
Country/TerritoryAustria
CitySalzburg
Period25/06/0327/06/03

Keywords

  • Cluster algorithm
  • Cluster validity
  • Data mining
  • Pattern recognition

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