Clustering Algorithm Based on Density Peak and Neighbor Optimization

The time complexity of density peak algorithm in selecting the cluster center is very high. It needs to manually select the cutoff distance. When processing the manifold data, there may be multiple density peaks, which leads to the decrease of clustering accuracy. In this paper, a new density peak clustering algorithm is proposed. This paper discusses and analyzes the clustering algorithm from three aspects of clustering center selection, outlier filtering and data point allocation. The clustering algorithm uses the KNN idea to calculate the density of data points in the selection of the cluster center. The screening and pruning of the outliers and the data point allocation are processed by the properties of the Voronoi diagram combined with the distribution characteristics of the data points. Finally, the hierarchical clustering idea is applied to merge similar clusters to improve clustering accuracy. The experimental results show that compared with the experimental comparison algorithms, the proposed algorithm has better clustering effect and accuracy