Explainable Clustering with CREAM

This paper proposes CREAM, a new explainable clustering technique based on decision tree induction, providing human-interpretable clusters by performing hypercubic approximations of the input feature space. CREAM may also be applied to data sets describing classification and regression tasks, given that the algorithm discriminates amongst input and output features. We also present OrCHiD, an automated tuning procedure to select the optimum CREAM parameter. Experiments demonstrating the effectiveness of CREAM in clustering, classification, and regression tasks are reported here, in comparison with other state-of-the-art techniques used as benchmarks.

parole chiave   Explainable AI, Applications that combine KR with machine learning, Integrating knowledge representation and machine learning, KR and machine learning, inductive logic programming, knowledge acquisition

presentazione di riferimento

 Explainable Clustering with CREAM (KR 2023, 07/09/2023) — Federico Sabbatini (Federico Sabbatini, Roberta Calegari)

evento origine

 KR 2023

progetto finanziatore

 TAILOR — Foundations of Trustworthy AI – Integrating Reasoning, Learning and Optimization  (01/09/2020–31/08/2024)

pubblicazione di riferimento per presentazione

 Explainable Clustering with CREAM (KR 2023, 07/09/2023) — Federico Sabbatini (Federico Sabbatini, Roberta Calegari)