A Variable Density‐Based Clustering Algorithm Based on Reverse Nearest Neighbors

ABSTRACT Traditional density‐based clustering algorithms often fail to identify cluster boundaries when dealing with variable density data, resulting in poor clustering outcomes. In this paper, a reverse nearest neighbor variable density clustering algorithm (RNNVDBC) with only a single undetermined parameter () is proposed to mitigate the issue. The proposed algorithm introduces a method based on reverse nearest neighbors (RNN) and density partition approach to accurately discover clusters of varying densities, shapes, and sizes as well as noise. To verify its effectiveness, experiments on various artificial and real‐world datasets with different densities are employed using RNNVDBC and the counterpart algorithms. The clustering validity metrics of adjusted rand index (ARI), normalized mutual information (NMI) and purity are implemented to evaluate their performance. The results demonstrate that the proposed algorithm is more effective to get the number and boundaries of clusters for varying density datasets better maintaining the integrity and separation between clusters.