ADBSCAN: Adaptive Density-Based Spatial Clustering of Applications with Noise for Identifying Clusters with Varying Densities

TL;DR

This paper introduces ADBSCAN, an adaptive version of DBSCAN, designed to effectively identify clusters with varying densities, overcoming the limitations of traditional DBSCAN in handling non-uniform data distributions.

Contribution

The paper presents a novel adaptive DBSCAN algorithm that improves clustering performance on datasets with clusters of different densities.

Findings

Enhanced clustering accuracy for variable-density datasets

Effective noise detection across diverse cluster densities

Demonstrated superior performance over traditional DBSCAN

Abstract

Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm which has the high-performance rate for dataset where clusters have the constant density of data points. One of the significant attributes of this algorithm is noise cancellation. However, DBSCAN demonstrates reduced performances for clusters with different densities. Therefore, in this paper, an adaptive DBSCAN is proposed which can work significantly well for identifying clusters with varying densities.