Cluster Analysis Based on Artificial Immune System and Ant Algorithm

Authors:
Chui-Yu Chiu;Chia-Hao Lin
Affiliations:
National Taipei University of Technology, Taiwan;National Taipei University of Technology, Taiwan
Venue:
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
Year:
2007

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Abstract

Ant algorithm is a meta-heuristic approach successfully applied to solve hard combinatorial optimization problems. It is also feasible for clustering analysis in data mining. Many researches use ant algorithms for clustering analysis and the result is better than other heuristic methods. In order to improve the performance of the algorithm, the artificial immune system is utilized to strengthen the ant algorithm for clustering analysis. In this paper, we proposes a new algorithm for clustering problem, the immunity-based Ant Clustering Algorithm (IACA). IACA using the artificial immune system and ant algorithm is an auto-clustering method which can decide the number of the clusters and its centroids. In this research, the proposed algorithm and these two clustering methods will be verified by 243 data sets are generated by Monte Carlo method to evaluate the performance of our proposed method and other methods.