Graph Clustering
Mohammad Asadpour; Shahin Pourbahrami
Abstract
Background and Objectives: One of the most important clustering methods is density-based clustering. This technique operates on the idea that clusters are regions of higher data density, separated by areas of lower density. Density Peak Clustering (DPC) is a modern density-based algorithm designed to ...
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Background and Objectives: One of the most important clustering methods is density-based clustering. This technique operates on the idea that clusters are regions of higher data density, separated by areas of lower density. Density Peak Clustering (DPC) is a modern density-based algorithm designed to efficiently identify cluster centers by constructing a decision graph. In this graph, points with high local density and a large distance from other high-density points are selected as cluster centers. Once these centers are determined, the remaining non-central points are assigned to clusters based on their proximity to the nearest center. However, DPC performs poorly on manifold datasets with varying densities and is highly sensitive to the selection of the cut-off distance parameter. Methods: To address these limitations and improve clustering performance, this study introduces an approach that employs the radial distribution function to quantify the relationship between data points and high-density regions. This method enables the estimation of the probability of finding neighboring points around a central or dense point, and a histogram is generated to represent these relationships. Results: Unlike traditional DPC, the proposed method eliminates the need for a distance cut-off parameter. The approach was implemented using the natural neighbor algorithm and the radial distribution function in a MATLAB environment. Conclusion: Experimental results demonstrated significant improvements in clustering accuracy and reductions in execution time compared to existing methods.
