Abstract: Aiming at the problem of slow mining efficiency and low accuracy in traditional clustering algorithm, we proposed a multilevel k-means clustering algorithm based on minimum spanning tree, and applied to data mining. Firstly, we analyzed the 
data types of the clustering samples and designed the clustering criterion function according to the analysis results. Secondly, we divided the sample data by the minimum spanning tree, and selected the initial clustering center. The data space of the sample was divided into rectangular unit,  the sample object data was calculated, descended and selected in the rectangular unit, the effective initial clustering center was obtained to reduce the time spent in data mining. The experimental results show that, compared with the traditional algorithm, the proposed method can quickly and accurately excavate the data, and the efficiency of mining is increased by about 50%.

Key words: minimum spanning tree, multilevel k-means clustering algorithm, data mining