Hierarchical clustering that takes advantage of both density-peak and density-connectivity

TL;DR

This paper introduces DC-HDP, a hierarchical density-based clustering algorithm that combines strengths of DP and DBSCAN to effectively detect arbitrarily shaped and varied density clusters, providing richer hierarchical results.

Contribution

The paper proposes a novel hierarchical clustering method, DC-HDP, that overcomes limitations of existing density-based algorithms by integrating their advantages.

Findings

DC-HDP outperforms 7 state-of-the-art algorithms on 14 datasets.

It effectively detects clusters with arbitrary shapes and varied densities.

Provides richer hierarchical clustering structures.

Abstract

This paper focuses on density-based clustering, particularly the Density Peak (DP) algorithm and the one based on density-connectivity DBSCAN; and proposes a new method which takes advantage of the individual strengths of these two methods to yield a density-based hierarchical clustering algorithm. Our investigation begins with formally defining the types of clusters DP and DBSCAN are designed to detect; and then identifies the kinds of distributions that DP and DBSCAN individually fail to detect all clusters in a dataset. These identified weaknesses inspire us to formally define a new kind of clusters and propose a new method called DC-HDP to overcome these weaknesses to identify clusters with arbitrary shapes and varied densities. In addition, the new method produces a richer clustering result in terms of hierarchy or dendrogram for better cluster structures understanding. Our empirical evaluation results show that DC-HDP produces the best clustering results on 14 datasets in comparison with 7 state-of-the-art clustering algorithms.