A Self-Representation Weighted-Based Density Peaks Clustering Method

Abstract

The approach to calculating density significantly impacts the clustering efficacy of the Density Peak Clustering (DPC) method, with various density calculation methods tailored for different datasets. To address this, this study introduces a Self-Representation Weighted Density Peak Clustering (SR-DPC) method. Unlike traditional DPC, SR-DPC not only utilizes the local data point information but also amplifies the impact of different data points on the data center by implementing a weighting strategy, enhancing the precision with which data centers are identified. Moreover, SR-DPC adaptively reflects the influence of diverse data points on the data centers via feature representation and employs a weighted Gaussian kernel distance instead of the Euclidean distance to boost its clustering capabilities. Experimental evaluations on both synthetic and real datasets demonstrate the effectiveness and practicality of the SR-DPC method.

Keywords

• DPC
• self-representation weighted
• feature representation
• adaptively
• the weighted Gaussian kernel distance