Research of Density Peaks Clustering Algorithm Based on Second-Order k Neighbors

Abstract

Clustering by fast search and find of density peaks (DPC) is a new density clustering algorithm proposed in recent years. The core of the algorithm is based on local density and relative distance. By drawing a decision diagram, the cluster center is selected manually, and the clustering is completed. The DPC algorithm uses the cutoff distance to calculate the local density, and essentially only considers the number of neighboring nodes around it, and the algorithm uses a single-step allocation strategy, which limits the accuracy and effectiveness of the algorithm for any data set to a certain extent. To solve the above problems, this paper proposes an optimized density peaks clus-tering algorithm based on second-order k neighbors (SODPC). The algorithm calculates direct density and indirect density by introducing second-order k neighbors of nodes, redefines the calculation method of local density, and on this basis, it defines the multi-step allocation strategy of non-central nodes to complete the clustering. Through manual and real data tests, it is proven that the algorithm in this paper has a good clustering effect on irregular and uneven density data sets.

Keywords

• density peaks clustering
• decision graph
• second-order k neighbor
• local density