A Quick Ant Clustering Algorithm

Enlightened by the behaviors of gregarious ant colonies, a quick and effective ant clustering (QAC) algorithm is presented. In the algorithm, each ant is treated as an agent to represent a data object. It will decide its next moving position according to similarity function and probability converting function between it and its neighbors. At the same time it will update its cluster number according to clustering rules. Each ant depends on a little local information to cluster quickly. The paper also gives the method of setting parameters which can resolve better the contradiction between converging speed and clustering quality. The QAC algorithm can increase clustering speed obviously and improve clustering quality effectively.