Parallel Artificial Immune Clustering Algorithm Based on Granular Computing

Authors:
Keming Xie;Xiaoli Hao;Jun Xie
Affiliations:
College of Information Engineering, Taiyuan University of Technology, 030024 Taiyuan, Shanxi, P.R. China;College of Computer and Software, Taiyuan University of Technology, 030024 Taiyuan, Shanxi, P.R. China;College of Information Engineering, Taiyuan University of Technology, 030024 Taiyuan, Shanxi, P.R. China
Venue:
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Year:
2009

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Abstract

When samples number, classification and dimension of clustering are much more, traditional clustering algorithm usually leads to unharmonious character between clustering and transcendent knowledge. Therefore, a new clustering algorithm is proposed, which is parallel artificial immune clustering algorithm based on granular computing. Artificial immune system model has the characteristics, such as parallel, random searching and maintaining diversity, which can solve premature problem in latter evolution and converge to a global optimization solution faster. Besides, we unite it to dynamic granulation model and apply granulation description to clustering. In the process of granulation changing, we can choose appropriate granulation size by adjusting to ensure clustering efficiency and quality. Tests show that the algorithm is more effective and more reasonable when we handle clustering of some data with it.