Abstract: Huge amounts of telecom data are generated every day, so how to extract useful information from the data is one of the data mining problems. Because different applications need different clusters, sometimes a single K-means cluster algorithm cannot generate userspecified K-clusters. An adjustable multitimes clustering mining method is proposed. A big value K was used in the K-means clustering algorithm for the first time, and K clusters were obtained. They were used to select the number of the clusters and the initial centers of the clusters for the second time. The experimental results show that our method is effective, and it can be applied to mining different amounts of clusters and big data analysis.

Key words: telecom data, multitimes clustering, K-means clustering, customer subdivision