A Dynamic Density Peak Clustering Algorithm Based on K-Nearest Neighbor

Abstract

The clustering results of the density peak clustering algorithm (DPC) are greatly affected by the parameter d c , and the clustering center needs to be selected manually. To solve these problems, this paper proposes a low parameter sensitivity dynamic density peak clustering algorithm based on K-Nearest Neighbor (DDPC), and the clustering label is allocated adaptively by analyzing the distribution of K-Nearest Neighbors around each data. It reduces the parameter sensitivity and eliminates selecting the clustering centers manually from the decision graph. Through the experimental analysis and comparison of the artificial dataset and UCI dataset, the results show that the comprehensive clustering effect of DDPC is better than DPC, DBSCAN, DBC, and other algorithms.