关键词: 半监督学习, K-均值聚类, 标签样本, 最小生成树

Abstract: K-means clustering algorithm acquires the number of clusters in advance.The random selection of the initial cluster centers will result in the instability and K-means clustering algorithm will be terminated in access to a local optimum value.In order to solve the problem，the improved K-means clustering algorithm based on semi-supervised learning theory obtains the number of clustering and initial clustering centers after building minimum spanning tree used by few label samples and splitting it iteratively.Although minimum spanning tree making up of random samples and initial clustering centers are different，the clustering is consistent and stable；the iteration is less than traditional K-means algorithm.It proves that the semi-supervised improved K-means algorithm is effective.

Key words: semi-supervised learning, K-means clustering, labeled sample, minimum spanning tree