Symmetry as a new measure for cluster validity

Chien Hsing Chou, Mu Chun Su, Eugene Lai

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

49 Scopus citations


In this paper, a cluster validity measure is presented to infer the appropriateness of data partitions. The proposed validity measure adopts a novel non-metric distance measure based on the idea of "point symmetry". The proposed validity measure can be applied in finding the number of clusters of different geometrical structures. The performance evaluation of the validity measure compares favorably to that of several validity functions and shows the effectiveness.

Original languageEnglish
Title of host publicationRecent Advances in Computers, Computing and Communications
PublisherWorld Scientific and Engineering Academy and Society
Number of pages5
ISBN (Print)9608052629
StatePublished - 2002


  • Cluster validity
  • Clustering algorithm
  • Pattern recognition
  • Similarity measure

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