Density Peak clustering algorithm based on t-SNE Optimization

Abstract

Aiming at the defect that the fast peak search clustering does not adapt to high-dimensional data sets, a T-DPC optimization algorithm is proposed. The algorithm is based on the t-SNE dimensionality reduction method, and also optimizes the calculation method of Gaussian kernel function, using a uniform metric when solving the density. Finally, the T-DPC algorithm and the DPC algorithm are compared in the artificial data set and the UCI standard data set, respectively, The experimental results show that the T-DPC algorithm not only adapts to the high dimensional dataset, but also improves the efficiency of the DPC algorithm.