A Self-Representation Weighted based Density Peaks Clustering Method

Abstract

The calculation method of density has an important influence on the clustering performance of density peak clustering method (DPC), the different density calculation methods are applied for the different datasets. To solve this problem, a self-representation weighted density peak clustering method (SR-DPC) is proposed in this study. Different from DPC, SR-DPC not only considers the local information of data points, but also enhances the influence of different data points on the data center by introducing the idea of weighting, so as to improve the accuracy of finding the data center.Furthermore, SR-DPC can adaptively reflect the influence of different data points on the data center points through the feature representation among data points, and the weighted Gaussian kernel distance is adopted to replace the Euclidean distance, so as to improve its clustering performance. Experimental results based on synthetic datasets and real datasets show that SR-DPC method is effective and practical.