ExACT Explainable Clustering: Unravelling the Intricacies of Cluster Formation

Cluster assignments, in particular the deep clustering ones, are often hard to explain, partially because they depend on all the features of the data in a complicated way, so it is difficult to determine why a particular row of data is classified in a particular bucket. This opaqueness makes their predictions not trustable, as for many predictors based on black boxes. This paper aims to tackle the aforementioned issues by introducing the design and implementation of ExACT, a new explainable clustering algorithm based on the induction of decision trees and performing hypercubic approximations of the input feature space in order to provide output human-interpretable clusters. Furthermore, ExACT is versatile enough to perform explainable classification and regression as well, as demonstrated in this work, proving to be a competitive alternative to existing analogous algorithms.