K-mean algorithm for optimizing the number of clusters based on particle swarm optimization
基于微粒群优化聚类数目的K–均值算法

K-mean algorithm is a widely used clustering method, but it is difficult to determine the number of clusters; and the clustering result is sensitive to initial cluster centers. We present a novel K-mean algorithm for optimizing the number of clusters based on particle swarm optimization. The algorithm denotes the position of a particle with the coordinates of cluster centers and wildcards. The coordinates of cluster centers are dynamically djusted by defining the new plus and new minus operators in the particle update formula. In addition, an improved Davies-Bouldin index is employed to evaluate the efficiency of a clustering result. Experimental results of 5 sets of artificial and real-world data validate the advantages of the proposed algorithm.