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

arXiv: https://arxiv.org/abs/1810.03393 · HTML: https://mlsanity.com/paper/1810.03393/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2018.

## Built on by (most cited first, 3 of 3)

- [SDC-HSDD-NDSA: Structure Detecting Cluster by Hierarchical Secondary Directed Differential with Normalized Density and Self-Adaption](https://mlsanity.com/paper/2307.00677/) (2023)
- [Rethinking Divisive Hierarchical Clustering from a Distributional Perspective](https://mlsanity.com/paper/2601.19718/) (2026)
- [Density-aware Hierarchical Clustering Based on Element-Categorized Connection Subgraphs](https://mlsanity.com/paper/2608.06990/) (2026)

## Abstract (arXiv)

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.