Explainable Rule-Based Clustering based on Cyclic Probabilistic Causal Models

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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.

Original languageEnglish
Title of host publication2020 24th International Conference Information Visualisation, IV 2020
EditorsEbad Banissi, Farzad Khosrow-Shahi, Anna Ursyn, Mark W. McK. Bannatyne, Joao Moura Pires, Nuno Datia, Kawa Nazemi, Boris Kovalerchuk, John Counsell, Andrew Agapiou, Zora Vrcelj, Hing-Wah Chau, Mengbi Li, Gehan Nagy, Richard Laing, Rita Francese, Muhammad Sarfraz, Fatma Bouali, Gilles Venturin, Marjan Trutschl, Urska Cvek, Heimo Muller, Minoru Nakayama, Marco Temperini, Tania Di Mascio, Filippo Sciarrone Veronica Rossano Rossano, Ralf Dorner, Loredana Caruccio, Autilia Vitiello, Weidong Huang, Michele Risi, Ugo Erra, Razvan Andonie, Muhammad Aurangzeb Ahmad, Ana Figueiras, Mabule Samuel Mabakane
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728191348
Publication statusPublished - Sep 2020
Event24th International Conference Information Visualisation, IV 2020 - Melbourne, Australia
Duration: 7 Sep 202011 Sep 2020

Publication series

NameProceedings of the International Conference on Information Visualisation
ISSN (Print)1093-9547


Conference24th International Conference Information Visualisation, IV 2020


  • categorization
  • clustering
  • concept
  • visualization


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