MDL for FCA: is there a place for background knowledge?

Tatiana Makhalova 1, 2 Sergei Kuznetsov 1 Amedeo Napoli 2
2 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : The Minimal Description Length (MDL) principle is a powerful and well founded approach, which has been successfully applied in a wide range of Data Mining tasks. In this paper we address the problem of pattern mining with MDL. We discuss how constraints-background knowledge on interestingness of patterns-can be embedded into MDL and argue the benefits of MDL over a simple selection of patterns based on measures.
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Submitted on : Friday, October 5, 2018 - 10:05:14 AM
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Tatiana Makhalova, Sergei Kuznetsov, Amedeo Napoli. MDL for FCA: is there a place for background knowledge?. IJCAI ECAI 2018 - 6th International Workshop "What can FCA do for Artificial Intelligence?", Jul 2018, Stockholm, Sweden. ⟨hal-01888440⟩



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