A clonal selection clustering algorithm using pointed symmetry-based distance measure

Ruochen Liu, Hejun Ning, Wei Zhang, Licheng Jiao

Abstract: A clonal selection clustering algorithm using point symmetry-based distance measure (CSCAPS) is proposed in this paper, a point symmetry-based similarity measure is used to evaluate the similarity between two samples in order to cluster data sets with the character of symmetry. Both Kd-trees based nearest neighbor search and k-nearest-neighbor consistency strategy are used to reduce the computation complexity and improve the clustering accuracy. The proposed method has been extensively compared with four well-known clustering algorithms over a test suit of real life data sets and synthetic data sets. The results of experiments indicate the superiority of the CSCAPS on accuracy.