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Conference Papers Year : 2018

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

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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Dates and versions

hal-01888440 , version 1 (05-10-2018)

Identifiers

  • HAL Id : hal-01888440 , version 1

Cite

Tatiana Makhalova, Sergei O. 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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