ECAI-2002 Conference Paper

[full paper]

A new clustering algorithm based on the ants chemical recognition system

Nicolas Labroche, Nicolas Monmarche, Gilles Venturini

In this paper, we introduce a new method to solve the unsupervised clustering problem, based on a modelling of the chemical recognition system of ants. This system allow ants to discriminate between nestmates and intruders, and thus to create homogeneous groups of individuals sharing a similar odor by continuously exchanging chemical cues. This phenomenon, known as "colonial closure", inspired us into developing a new clustering algorithm and then comparing it to a well-known method such as K-Means method. Our results show that our algorithm performs better than K-Means over artificial and real data sets, and furthermore our approach requires less initial information (such as number of classes, shape of classes, limitation in the types of attributes handled).

Keywords: Artificial ants, Clustering, Machine Learning, Autonomous Agents, Multi-Agent Systems

Citation: Nicolas Labroche, Nicolas Monmarche, Gilles Venturini: A new clustering algorithm based on the ants chemical recognition system. In F. van Harmelen (ed.): ECAI2002, Proceedings of the 15th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2002, pp.345-349.