Density Peak Clustering Algorithm Based on Shared Neighborhood - LI Fuxiang; ZHOU Ming; YANG Tianhao (Journal of Harbin University of Science and Technology)
To solve the problem of poor clustering performance and automatic determination of cluster center inthe processing of unevenly distributed datasets, a density peak clustering algorithm based on shared neighborhood is proposed ( DPC-SN) . Firstly, considering the local neighborhood information of the data points and the correlation between data points, the local density is redefined according to the shared neighborhood. Secondly, a new decision threshold is given as the critical value to distinguish cluster centers from non-cluster centers, and the cluster centers are automatically obtained. Finally, experiments are performed on synthetic datasets and UCI datasets with different distribution characteristics. The results show that the clustering accuracy and overall performance of this algorithm are better than four algorithms: DPC-KNN, DPC, K-means and DBSCAN.