A new measure of cluster validity using line symmetry

Chien Hsing Chou, Yi Zeng Hsieh, Mu Chun Su

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Many real-world and man-made objects are symmetry, therefore, it is reasonable to assume that some kind of symmetry may exist in data clusters. In this paper a new cluster validity measure which adopts a non-metric distance measure based on the idea of "line symmetry" is presented. The proposed validity measure can be applied in finding the number of clusters of different geometrical structures. Several data sets are used to illustrate the performance of the proposed measure.

Original languageEnglish
Pages (from-to)443-461
Number of pages19
JournalJournal of Information Science and Engineering
Issue number2
StatePublished - Mar 2014


  • Cluster analysis
  • Cluster validity
  • Clustering algorithm
  • Line symmetry
  • Similarity measure
  • Unsupervised learning


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