# DECWA : Density-Based Clustering using Wasserstein Distance

arXiv: https://arxiv.org/abs/2310.16552 · HTML: https://mlsanity.com/paper/2310.16552/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 25 references that are in the map.
- First posted on arXiv 25 Oct 2023.

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

- [A New Robust Partial p-Wasserstein-Based Metric for Comparing Distributions](https://mlsanity.com/paper/2405.03664/) (2024)
- [Learning Order Forest for Qualitative-Attribute Data Clustering](https://mlsanity.com/paper/2603.03387/) (2026)
- [Hybrid Intent-Aware Personalization with Machine Learning and RAG-Enabled Large Language Models for Financial Services Marketing](https://mlsanity.com/paper/2603.14173/) (2026)

## Abstract (arXiv)

Clustering is a data analysis method for extracting knowledge by discovering groups of data called clusters. Among these methods, state-of-the-art density-based clustering methods have proven to be effective for arbitrary-shaped clusters. Despite their encouraging results, they suffer to find low-density clusters, near clusters with similar densities, and high-dimensional data. Our proposals are a new characterization of clusters and a new clustering algorithm based on spatial density and probabilistic approach. First of all, sub-clusters are built using spatial density represented as probability density function (p.d.f) of pairwise distances between points. A method is then proposed to agglomerate similar sub-clusters by using both their density (p.d.f) and their spatial distance. The key idea we propose is to use the Wasserstein metric, a powerful tool to measure the distance between p.d.f of sub-clusters. We show that our approach outperforms other state-of-the-art density-based clustering methods on a wide variety of datasets.