TY - GEN

T1 - Explainable Rule-Based Clustering based on Cyclic Probabilistic Causal Models

AU - Vityaev, Evgenii E.

AU - Pak, Bayar

N1 - Publisher Copyright: © 2020 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2020/9

Y1 - 2020/9

N2 - Discovering and visualizing data clusters is an important AI/ML and visual knowledge discovery task. This paper proposes a new data clustering approach inspired by the concept of causal models used in cognitive science. This approach is based on the causal relations between features, instead of similarity of features in traditional clustering approaches. The concept of the center of the cluster is formalized in accordance with prototype theory of concepts explored in the cognitive science in terms of a correlational structure of perceived attributes. Traditionally in AI and cognitive science, causal models are described using Bayesian networks. However, Bayesian networks do not support cycles. This paper proposes a novel mathematical apparatus probabilistic generalization of formal concepts-for describing causal models via cyclical causal relations (fixpoints of causal relations) that form a clusters and generate a clusters prototypes. This approach is illustrated with a case study.

AB - Discovering and visualizing data clusters is an important AI/ML and visual knowledge discovery task. This paper proposes a new data clustering approach inspired by the concept of causal models used in cognitive science. This approach is based on the causal relations between features, instead of similarity of features in traditional clustering approaches. The concept of the center of the cluster is formalized in accordance with prototype theory of concepts explored in the cognitive science in terms of a correlational structure of perceived attributes. Traditionally in AI and cognitive science, causal models are described using Bayesian networks. However, Bayesian networks do not support cycles. This paper proposes a novel mathematical apparatus probabilistic generalization of formal concepts-for describing causal models via cyclical causal relations (fixpoints of causal relations) that form a clusters and generate a clusters prototypes. This approach is illustrated with a case study.

KW - categorization

KW - clustering

KW - concept

KW - visualization

UR - http://www.scopus.com/inward/record.url?scp=85102919771&partnerID=8YFLogxK

U2 - 10.1109/IV51561.2020.00139

DO - 10.1109/IV51561.2020.00139

M3 - Conference contribution

AN - SCOPUS:85102919771

T3 - Proceedings of the International Conference on Information Visualisation

SP - 307

EP - 312

BT - 2020 24th International Conference Information Visualisation, IV 2020

A2 - Banissi, Ebad

A2 - Khosrow-Shahi, Farzad

A2 - Ursyn, Anna

A2 - McK. Bannatyne, Mark W.

A2 - Pires, Joao Moura

A2 - Datia, Nuno

A2 - Nazemi, Kawa

A2 - Kovalerchuk, Boris

A2 - Counsell, John

A2 - Agapiou, Andrew

A2 - Vrcelj, Zora

A2 - Chau, Hing-Wah

A2 - Li, Mengbi

A2 - Nagy, Gehan

A2 - Laing, Richard

A2 - Francese, Rita

A2 - Sarfraz, Muhammad

A2 - Bouali, Fatma

A2 - Venturin, Gilles

A2 - Trutschl, Marjan

A2 - Cvek, Urska

A2 - Muller, Heimo

A2 - Nakayama, Minoru

A2 - Temperini, Marco

A2 - Di Mascio, Tania

A2 - Rossano, Filippo Sciarrone Veronica Rossano

A2 - Dorner, Ralf

A2 - Caruccio, Loredana

A2 - Vitiello, Autilia

A2 - Huang, Weidong

A2 - Risi, Michele

A2 - Erra, Ugo

A2 - Andonie, Razvan

A2 - Ahmad, Muhammad Aurangzeb

A2 - Figueiras, Ana

A2 - Mabakane, Mabule Samuel

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 24th International Conference Information Visualisation, IV 2020

Y2 - 7 September 2020 through 11 September 2020

ER -