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Conference Papers Year : 2019

On Coupling FCA and MDL in Pattern Mining

Abstract

Pattern Mining is a well-studied field in Data Mining and Machine Learning. The modern methods are based on dynamically updating models, among which MDL-based ones ensure high-quality pattern sets. Formal concepts also characterize patterns in a condensed form. In this paper we study MDL-based algorithm called Krimp in FCA settings and propose a modified version that benefits from FCA and relies on probabilistic assumptions that underlie MDL. We provide an experimental proof that the proposed approach improves quality of pattern sets generated by Krimp.
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Dates and versions

hal-02162928 , version 1 (23-06-2019)

Identifiers

  • HAL Id : hal-02162928 , version 1

Cite

Tatiana Makhalova, Sergei O. Kuznetsov, Amedeo Napoli. On Coupling FCA and MDL in Pattern Mining. ICFCA 2019 - 15th International Conference on Formal Concept Analysis, Jun 2019, Frankfurt, Germany. pp.332-340. ⟨hal-02162928⟩
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