Abstract: Cluster analysis is one of the most important fields of data mining.K-means algorithm has important value in data mining.K-means must be given the number of clusters and it forms local convergence easily.So a new clustering algorithm，K-means based on the Shared Nearest Neighbor（KSNN），is designed.KSNN finds the core nodes of the data to get the number of clusters and takes it as the parameter for K-means automatically.It conquers the problem that the number of clusters to K-means must be defined by humans，meanwhile it has better global convergence.Experimental results show that KSNN is more effective than K-means，Particle Swarm Optimal（PSO） and multiseed core algorithm（MCA）.

摘要： 聚类分析是一种重要的数据挖掘方法。K-means聚类算法在数据挖掘领域具有非常重要的应用价值。针对K-means需要人工设定聚类个数并且易陷入局部极优的缺陷，提出了一种基于最近共享邻近节点的K-means聚类算法（KSNN）。KSNN在数据集中搜索中心点，依据中心点查找数据集个数，为K-means聚类提供参数。从而克服了K-means需要人工设定聚类个数的问题，同时具有较好的全局收敛性。实验证明KSNN算法比K-means、粒子群K-means（pso）以及多中心聚类算法（MCA）有更好的聚类效果。