# On the use of Wasserstein metric in topological clustering of distributional data

arXiv: https://arxiv.org/abs/2109.04301 · HTML: https://mlsanity.com/paper/2109.04301/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 35 references that are in the map.
- First posted on arXiv 9 Sep 2021.

## Built on by (most cited first, 1 of 1)

- [Gamifying optimization: a Wasserstein distance-based analysis of human search](https://mlsanity.com/paper/2112.06292/) (2021)

## Abstract (arXiv)

This paper deals with a clustering algorithm for histogram data based on a Self-Organizing Map (SOM) learning. It combines a dimension reduction by SOM and the clustering of the data in a reduced space. Related to the kind of data, a suitable dissimilarity measure between distributions is introduced: the L2 Wasserstein distance. Moreover, the number of clusters is not fixed in advance but it is automatically found according to a local data density estimation in the original space. Applications on synthetic and real data sets corroborate the proposed strategy.