关键词: 粒子群聚类算法, K-means算法, 自适应K值, 收敛

Abstract: Traditional K-means algorithm is not only sensitive to the choice of initial clustering center and is easy to converge to the local optimal solution, but also has the problem of determining the value of K：inappropriate K values often lead to poor clustering results. The K value problem is an important research direction of clustering analysis, on the basis of particle clustering algorithm, combining the K-means algorithm, this paper proposes the particle swarm optimization clustering algorithm with adaptive K values. When the algorithm convergence, by comparing the relationship between different values of global optimal fitness under different K values, the increase or decrease of K values can be detemined. Experiments show that the improved algorithm can guide the selection of K values, and has a better clustering effect.

Key words: particle swarm optimization algorithm, K-means algorithm, self-adaptive K values, convergence