How Your Supporters and Opponents Define Your Interestingness

Bruno Crémilleux 1 Arnaud Giacometti 2 Arnaud Soulet 3
1 Equipe CODAG - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
3 BDTLN - Bases de données et traitement des langues naturelles
LIFAT - Laboratoire d'Informatique Fondamentale et Appliquée de Tours
Abstract : How can one determine whether a data mining method ex- tracts interesting patterns? The paper deals with this core question in the context of unsupervised problems with binary data. We formalize the quality of a data mining method by identifying patterns – the supporters and opponents – which are related to a pattern extracted by a method. We define a typology offering a global picture of the methods based on two complementary criteria to evaluate and interpret their interests. The quality of a data mining method is quantified via an evaluation com- plexity analysis based on the number of supporters and opponents of a pattern extracted by the method. We provide an experimental study on the evaluation of the quality of the methods.
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Submitted on : Saturday, October 6, 2018 - 11:17:59 AM
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Bruno Crémilleux, Arnaud Giacometti, Arnaud Soulet. How Your Supporters and Opponents Define Your Interestingness. ECML-PKDD The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2018, Dublin, Ireland. pp.373-389. ⟨hal-01889234⟩



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