The cluster center is chosen manually in the decision graph of the clustering by fast search and find of peaks (CFSFDP) algorithm. And when the cluster is divided into cluster core and cluster halo, some points on the edge of the cluster are divided into the cluster halo group, leading to unreasonable division results. To solve the above problem, we propose a clustering algorithm for automatic selection of cluster center and optimization of the division of cluster core and cluster halo. We adopt the idea of anomaly detection to find the anomaly points of the cluster center. The anomaly points are regarded as the cluster center. And local density in the cluster is introduced for the optimization of the division of cluster core and cluster halo. Comparative experiments show that the proposed algorithm is superior to the CFSFDP algorithm in automation effect and the clustering results are more accurate.

 

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