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ExACT Explainable Clustering: Unravelling the Intricacies of Cluster Formation
Joint Proceedings of the 2nd Workshop on Knowledge Diversity and the 2nd Workshop on Cognitive Aspects of Knowledge Representation co-located with 20th International Conference on Principles of Knowledge Representation and Reasoning (KR2023), pages 3:1–3:8
CEUR Workshop Proceedings
2023
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.
Joint Proceedings of the 2nd Workshop on Knowledge Diversity and the 2nd Workshop on Cognitive Aspects of Knowledge Representation co-located with 20th International Conference on Principles of Knowledge Representation and Reasoning (KR2023)
KoDis 2023 @ KR 2023
CEUR Workshop Proceedings (CEUR-WS.org)
AEQUITAS — Assessment and Engineering of eQuitable, Unbiased, Impartial and Trustworthy Ai Systems (01/11/2022–31/10/2025)
ExACT Explainable Clustering: Unravelling the Intricacies of Cluster Formation (KoDis 2023 @ KR 2023, 03/09/2023) — Federico Sabbatini