# A Distributional Approach for Soft Clustering Comparison and Evaluation

arXiv: https://arxiv.org/abs/2206.09827 · HTML: https://mlsanity.com/paper/2206.09827/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 22 references that are in the map.
- First posted on arXiv 20 Jun 2022.

## Builds on (most cited first, 1 of 1)

- [Decision-Making with Belief Functions: a Review](https://mlsanity.com/paper/1808.05322/) (2018)

## Abstract (arXiv)

The development of external evaluation criteria for soft clustering (SC) has received limited attention: existing methods do not provide a general approach to extend comparison measures to SC, and are unable to account for the uncertainty represented in the results of SC algorithms. In this article, we propose a general method to address these limitations, grounding on a novel interpretation of SC as distributions over hard clusterings, which we call distributional measures. We provide an in-depth study of complexity- and metric-theoretic properties of the proposed approach, and we describe approximation techniques that can make the calculations tractable. Finally, we illustrate our approach through a simple but illustrative experiment.