Ant clustering embeded in cellular automata

Authors:
Xiaohua Xu;Ling Chen;Ping He
Affiliations:
Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, P.R. China;Department of Computer Science, Yangzhou University, Yangzhou, P.R. China;Department of Computer Science, Yangzhou University, Yangzhou, P.R. China
Venue:
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
Year:
2005

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Abstract

Inspired by the emergent behaviors of ant colonies, we present a novel ant algorithm to tackle unsupervised data clustering problem. This algorithm integrates swarm intelligence and cellular automata, making the clustering procedure simple and fast. It also avoid ants’ longtime idle moving, and show good separation of data classes in clustering visualization. We have applied the algorithm on the standard ant clustering benchmark and we get better results compared with the LF algorithm. Moreover, the experimental results on real world applications report that the algorithm is significantly more efficient than the previous approaches.