Density peaks clustering algorithm based on kernel density estimation and minimum spanning tree

Abstract

It is to be reported that a clustering algorithm that can achieve rapid searching of density peaks in 2014. Aiming at solving the shortcomings of the algorithm, this paper proposes a density peak clustering algorithm based on kernel density estimation and minimum spanning tree (MST). The proposed DPC algorithm adopts the Gaussian kernel density to estimate the local density of samples, coordinating the relationship between the part and the whole; proposing a new allocation strategy, which combines the idea of minimum spanning tree to generate a tree from the dataset according to the principle of high density and close distance. The degree of polymerisation is defined and calculated before and after disconnecting one edge of the tree, and the edge is disconnected with the larger degree of polymerisation, which until the expected number of clusters is met. The experimental results make known that the proposed algorithm has better clustering result.