evclust: Python library for evidential clustering
This provides a tool for researchers and practitioners in data science to handle uncertainty in clustering tasks, but it is incremental as it implements existing algorithms in a new software package.
The paper introduces evclust, a Python library for evidential clustering that tackles the problem of capturing uncertainty in cluster membership using Dempster-Shafer theory, resulting in a credal partition for data objects.
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.