Hybrid clustering algorithm based on the artificial immune principle

Abstract

A hybrid clustering algorithm based on the artificial immune theory is presented in this paper. The method is inspired by the clone selection and memory principle. The problem of local optimal can be avoided by introducing the differentiation of memory antibody and inhibition mechanism. In addition, the K-means algorithm is used as a search operator in order to improve the convergence speed. The proposed algorithm can obtain the better data convergence compared with the K-means algorithm based clustering approach and artificial immune based approach. Simulate experimental results indicate the hybrid algorithm has a faster convergence speed and the obtained clustering centers can get strong stability. © 2011 Springer-Verlag.