K-means algorithm is the most widely used method due to its easy understanding and fast speed.However

this method has the disadvantage that the clustering results depend on the selection of the initial clustering center and it is easy to fall into local optimal.For this reason

this paper proposed a honey-bee mating optimization clustering algorithm.It generates initial swarm by density and distance

and regards rough set clustering algorithm which has strong local search ability as a code of the works to enhance the local search ability of the algorithm.At last

in order to improve the diversity level of the swarm and the global optimization ability of the algorithm

random swarm population are introduced continuously in the iterative process.Our experiments show that the proposed algorithm not only can effectively suppress premature convergence

but also has strong stability and produces good clustering results.