AMD-DBSCAN: An Adaptive Multi-density DBSCAN for datasets of extremely variable density

TL;DR

AMD-DBSCAN is an adaptive clustering algorithm that effectively handles datasets with highly variable densities by optimizing parameters and reducing execution time, outperforming existing methods in accuracy.

Contribution

The paper introduces AMD-DBSCAN, a novel multi-density clustering algorithm with an improved parameter adaptation method and reduced hyperparameters, enhancing performance on variable density datasets.

Findings

Reduces execution time by 75% compared to traditional methods.

Improves clustering accuracy by 24.7% on multi-density datasets.

Maintains performance in single-density scenarios.

Abstract

DBSCAN has been widely used in density-based clustering algorithms. However, with the increasing demand for Multi-density clustering, previous traditional DSBCAN can not have good clustering results on Multi-density datasets. In order to address this problem, an adaptive Multi-density DBSCAN algorithm (AMD-DBSCAN) is proposed in this paper. An improved parameter adaptation method is proposed in AMD-DBSCAN to search for multiple parameter pairs (i.e., Eps and MinPts), which are the key parameters to determine the clustering results and performance, therefore allowing the model to be applied to Multi-density datasets. Moreover, only one hyperparameter is required for AMD-DBSCAN to avoid the complicated repetitive initialization operations. Furthermore, the variance of the number of neighbors (VNN) is proposed to measure the difference in density between each cluster. The experimental results show that our AMD-DBSCAN reduces execution time by an average of 75% due to lower algorithm complexity compared with the traditional adaptive algorithm. In addition, AMD-DBSCAN improves accuracy by 24.7% on average over the state-of-the-art design on Multi-density datasets of extremely variable density, while having no performance loss in Single-density scenarios. Our code and datasets are available at https://github.com/AlexandreWANG915/AMD-DBSCAN.