Self-adaption grey DBSCAN clustering

TL;DR

This paper introduces SAG-DBSCAN, a self-adapting clustering algorithm that combines grey relational analysis with DBSCAN to automatically identify noise and improve clustering accuracy.

Contribution

The paper proposes a novel self-adapting grey DBSCAN algorithm that automatically determines parameters and enhances clustering performance using grey relational analysis.

Findings

Demonstrates improved clustering accuracy on multiple datasets.

Outperforms several state-of-the-art clustering algorithms.

Effectively identifies noise and dense data subsets.

Abstract

Clustering analysis, a classical issue in data mining, is widely used in various research areas. This article aims at proposing a self-adaption grey DBSCAN clustering (SAG-DBSCAN) algorithm. First, the grey relational matrix is used to obtain the grey local density indicator, and then this indicator is applied to make self-adapting noise identification for obtaining a dense subset of clustering dataset, finally, the DBSCAN which automatically selects parameters is utilized to cluster the dense subset. Several frequently-used datasets were used to demonstrate the performance and effectiveness of the proposed clustering algorithm and to compare the results with those of other state-of-the-art algorithms. The comprehensive comparisons indicate that our method has advantages over other compared methods.