evclust: Python library for evidential clustering

TL;DR

evclust is a Python library that implements evidential clustering algorithms based on Dempster-Shafer theory, enabling uncertainty quantification in cluster assignments and providing visualization and evaluation tools.

Contribution

This paper introduces evclust, a Python library that offers efficient evidential clustering algorithms and tools for credal partition analysis based on belief functions.

Findings

Provides a suite of evidential clustering algorithms

Includes visualization and evaluation tools for credal partitions

Enhances uncertainty management in clustering tasks

Abstract

A recent developing trend in clustering is the advancement of algorithms that not only identify clusters within data, but also express and capture the uncertainty of cluster membership. Evidential clustering addresses this by using the Dempster-Shafer theory of belief functions, a framework designed to manage and represent uncertainty. This approach results in a credal partition, a structured set of mass functions that quantify the uncertain assignment of each object to potential groups. The Python framework evclust, presented in this paper, offers a suite of efficient evidence clustering algorithms as well as tools for visualizing, evaluating and analyzing credal partitions.