Artificial bee colony rough clustering algorithm based on mutative precision search

For the problems of the traditional K-means clustering algorithm such as depending overly on initial clustering centers, the poor global search ability and stability, an artificial bee colony rough clustering algorithm based on mutative precision search is proposed, which generates initial swarm by density and distance, and gets the selection probability of onlooker bees according to the fitness and density of lead bees, then updates scout bees through the method of mutative precision search, in order to avoid falling into local optimum. Finally, the rough set is combined to optimize K-means. The experiment results show that this algorithm not only can suppress the local convergence effectively and reduce the dependence on the initial cluster center, but also has higher accuracy and stronger stability than others.