K-means clustering algorithm based on bee colony strategy

Abstract

The traditional swarm intelligence optimization K-means algorithm has some problems, such as poor global search ability and blind area selection of initial center points, which leads to the reduction of clustering availability. In order to avoid the above limitations, this paper proposed IABC k-means algorithm. Firstly, the employed bees stage in the traditional artificial bee colony ABC algorithm uses the current colony optimal solution information to guide its optimization search. Secondly, this paper wanted to solve the search ability of the employed bees and expand the information sharing range between various individuals, a random guidance mechanism is proposed in the onlookers bees stage. Finally, chaotic sequence is introduced in the scout bee stage to accelerate the convergence speed of the algorithm. IABC algorithm is proposed and applied to K-means clustering algorithm to improve the poor global search ability of K-means algorithm and the random selection of initial center points. Experiments show that the IABC and IABCK-means proposed in this paper effectively improves the clustering availability.