Fast unsupervised clustering with artificial ants

Abstract

AntClust is a clustering algorithm that is inspired by the chemical recognition system of real ants. It associates the genome of each artificial ant to an object of the initial data set and simulates meetings between ants to create nests of individuals that share a similar genome. Thus, the nests realize a partition of the original data set with no hypothesis concerning the output clusters (number, shape, size ... ) and with minimum input parameters. Due to an internal mechanism of nest selection and finalization, AntClust runs in the worst case in quadratic time complexity with the number of ants. In this paper, we evaluate new heuristics for nest selection and finalization that allows AntClust to run on linear time complexity with the number of ants. © Springer-Verlag 2004.