Abstract: Traditional k-means algorithms usually ignores the distribution of the data samples,assign all of them in the cluster edge position,center position,outliers to the cluster which nearest clustering center locates,in accordance with the principle of minimum distance,without considering the relationsh1ip between the data sample and other clusters.If the distance between the data sample and the other cluster is close to the minimum distance,the data sample is very close to the two clusters,obviously,the direct division menthod is not reasonable.Aiming at this problem,this paper presented a clustering algorithm optimized nearest neighbor (1NN-kmeans).Using the ideas of neighbor,assign these samples that do not firmly belong to a certain cluster to the cluster that the nearest neighbor sample belongs to.The experimental results show that 1NN effectively reduced the number of iterations and improved the clustering accuracy and finally achieved the better clustering results.

Key words: Cluster, Distribution, K-means, Nearest neighbor, Relationship