Hierarchical clustering that takes advantage of both density-peak and density-connectivity
This work addresses the challenge of improving clustering accuracy for datasets with complex structures, though it appears incremental as it builds on existing methods.
The paper tackled the problem of density-based clustering by combining the strengths of Density Peak (DP) and DBSCAN to detect clusters with arbitrary shapes and varied densities, resulting in a new method called DC-HDP that produced the best clustering results on 14 datasets compared to 7 state-of-the-art algorithms.
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.