CluSim: a python package for calculating clustering similarity

Abstract

Clustering is a primary method to reveal the structure of data (Jain, Murty, & Flynn, 1999). To understand, evaluate, and leverage data clusterings, we need to quantitatively compare them. Clustering comparison is the basis for method evaluation, consensus clustering, and tracking the temporal evolution of clusters, among many other tasks. For instance, the evaluation of a clustering method is usually achieved by comparing the method’s result to a planted reference clustering, assuming that the more similar the method’s solution is to the reference clustering, the better the method. Despite the importance of clustering comparison, no consensus has been reached for a standardized assessment; each similarity measure rewards and penalizes different criteria, sometimes producing contradictory conclusions.