A New Clustering Algorithm Based on the Chemical Recognition System of Ants

Abstract

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).