Visual Clustering with Artificial Ants Colonies

In this paper, we propose a new model of the chemical recognition system of ants to solve the unsupervised clustering problem. The colonial closure mechanism allows ants to discriminate between nest-mates and intruders by the mean of a colonial odour that is shared by every nestmate. In our model we associate each object of the data set to the odour of an artificial ant. Each odour is defined as a real vector with two components, that can be represented in a 2D-space of odours. Our method simulates meetings between ants according to pre-established behavioural rules, to ensure the convergence of similar odours (i.e. similar objects) in the same portion of the 2D-space. This provides the expected partition of the objects. We test our method against other well-known clustering method and show that it can perform well. Furthermore, our approach can handle every type of data (from numerical to symbolic attibutes, since there exists an adapted similarity measure) and allows one to visualize the dynamic creation of the nests. We plan to use this algorithm as a basis for a more sophisticated interactive clustering tool.