A two-phase clustering algorithm based on artificial immune network

Authors:
Jiang Zhong;Zhong-Fu Wu;Kai-Gui Wu;Ling Ou;Zheng-Zhou Zhu;Ying Zhou
Affiliations:
College of Computer Science and Engineering, ChongQing University, ChongQing, China;College of Computer Science and Engineering, ChongQing University, ChongQing, China;College of Computer Science and Engineering, ChongQing University, ChongQing, China;College of Computer Science and Engineering, ChongQing University, ChongQing, China;College of Computer Science and Engineering, ChongQing University, ChongQing, China;College of Computer Science and Engineering, ChongQing University, ChongQing, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

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Abstract

This paper proposes a novel dynamic clustering algorithm called DCBAIN, which based on the artificial immune network and immune optimization algorithm. The algorithm includes two phases, it begins by running artificial immune network to find a clustering feasible solution (CFS), then it employs antibody clone algorithm (ACA) to get the optimal cluster number and cluster centers on the CFS. Some experimental results show that new algorithm has satisfied convergent probability and convergent speed.