A new ant colony algorithm for a general clustering

Ant colony algorithm (ACA), inspired by the food-searching behavior of ants, is an evolutionary algorithm and performs well in discrete optimization. In this paper, a new kind of general clustering algorithm based on ACA is presented according to the principle that how human do clustering and the action that how ants look for food. With this algorithm, we need not take some time to gain the initial clustering center, so it is a general method. According to the statistics, the subjective fact influencing on appraising results could be avoided. Moreover, we can obtain the interval of clustering radius through local search. Finally, this algorithm has been implemented and tested on a real datasets. The performance of this algorithm is compared with the other popular method, which used by [1]. Our computation simulations reveal very encouraging results in terms of clustering ability and the method is an efficient and effective approach.