Abstract: The traditional K-means algorithm is very sensitive to the selection of cluster initial points and the calculation of distance metrics，it is possible that the K-means algorithm can converge to local optimal solutions．Aiming at this problem，an improved K-means algorithm，namely K-means clustering algorithm optimal matching algorithm． The improved algorithm firstly selects the initial point of the traditional K-means clustering algorithm and analyzes the clustering result． Then，it conducts experimental tests from the selection of the initial clustering center and the determination of the distance algorithm respectively，and introduces the contour coefficient evaluation clustering． The experimental results prove that the optimal matching algorithm of K-means clustering algorithm has better stability and higher clustering accuracy．

Key words: K-means algorithm, clustering center, cluster analysis