Artificial Ants for Clustering with Adaptive Aggregation Conditions: Application to Image Clustering

The world of ants is a reach source of inspiration since real ants are able to solve collectively relatively complex problems. Particularly, several ant based clustering algorithms have been proposed in the literature. These clustering models were derived from several phenomena among real ants such as cemetery organization, recognition system, building alive structures, etc. In this work, we try to adapt the properties of sound communication among real ants to resolve the clustering problem. Artificial ants move randomly on a 2D toroidal grid where objects are initially scattered at random. They communicate with each others in order to recruit ants having similar heaps of objects. We have applied this algorithm on many databases and we get very good results compared to the K-means algorithm. An application to image clustering is also realized.