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.
This research was supported in part by Chinese National Science Foundation under contract 60473012 and Chinese National Foundation Science and Technology Development under contract 2003BA614A-14.
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Xu, X., Chen, L., He, P. (2005). Ant Clustering Embeded in Cellular Automata. In: Capcarrère, M.S., Freitas, A.A., Bentley, P.J., Johnson, C.G., Timmis, J. (eds) Advances in Artificial Life. ECAL 2005. Lecture Notes in Computer Science(), vol 3630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553090_57
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DOI: https://doi.org/10.1007/11553090_57
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