Detection of line-symmetry clusters

Yi Zeng Hsieh, Mu Chun Su, Chien Hsing Chou, Pa Chun Wang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Many real-world and man-made objects are symmetry. Therefore, it is reasonable to assume that some kinds of symmetry may exist in data clusters. The most common type of symmetry is line symmetry. In this paper, we propose a line symmetry distance measure. Based on the proposed line symmetry distance, a modified version of the K-means algorithm can be used to partition data into clusters with different geometrical shapes. Several data sets are used to test the performance of the proposed modified version of the K-means algorithm incorporated with the line symmetry distance.

Original languageEnglish
Pages (from-to)5027-5043
Number of pages17
JournalInternational Journal of Innovative Computing, Information and Control
Issue number8
StatePublished - Aug 2011


  • Cluster analysis
  • Clustering algorithm
  • Distance measure
  • Symmetry


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