• 人工智能 •

### 结合自然和共享最近邻的密度峰值聚类算法

1. 1. 长沙理工大学 计算机与通信工程学院，长沙 410114
2. 国网上海电力公司，上海 200000
• 出版日期:2021-05-01 发布日期:2021-04-30

### Peak Density Clustering Algorithm Combining Natural and Shared Nearest Neighbor

BAI Exiang, LUO Ke, LUO Xiao

1. 1. School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China
2. State Grid Shanghai Municipal Electric Power Company, Shanghai 200000, China
• Online:2021-05-01 Published:2021-04-30

Abstract:

The clustering by fast search and find of density peaks (DPC) has the advantages of no iteration and fewer parameters, but it still has some shortcomings: the need to manually select the cutoff distance parameter and the processing effect is not good on the manifold data set. In response to these problems, an improved density peak clustering algorithm is proposed. The algorithm combines the natural and shared nearest neighbor algorithm, redefines the calculation method of cut-off distance and local density. It integrates the concept of candidate cluster center calculation, selects different candidate cluster centers through the algorithm, uses these candidate centers as a new data set, and starts density peak clustering again. Finally, the remaining points are assigned to the clusters where the corresponding candidate center points are located. The improved algorithm is verified on the synthetic data set and UCI data set rows, and compared with the K-means, DBSCAN (density-based algorithm for discovering clusters in large spatial databases with noise) and DPC algorithm. Experimental results show that the algorithm proposed in this paper has significant improvement in performance.