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From-below Boolean matrix factorization algorithm based on MDL

Author

Listed:
  • Tatiana Makhalova

    (National Research University Higher School of Economics
    University of Lorraine)

  • Martin Trnecka

    (Palacký University Olomouc)

Abstract

During the past few years Boolean matrix factorization (BMF) has become an important direction in data analysis. The minimum description length principle (MDL) was successfully adapted in BMF for the model order selection. Nevertheless, a BMF algorithm performing good results w.r.t. standard measures in BMF is missing. In this paper, we propose a novel from-below Boolean matrix factorization algorithm based on formal concept analysis. The algorithm utilizes the MDL principle as a criterion for the factor selection. On various experiments we show that the proposed algorithm outperforms—from different standpoints—existing state-of-the-art BMF algorithms.

Suggested Citation

  • Tatiana Makhalova & Martin Trnecka, 2021. "From-below Boolean matrix factorization algorithm based on MDL," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(1), pages 37-56, March.
    • Handle: RePEc:spr:advdac:v:15:y:2021:i:1:d:10.1007_s11634-019-00383-6
    DOI: 10.1007/s11634-019-00383-6
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    References listed on IDEAS

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    1. Govaert, Gérard & Nadif, Mohamed, 2008. "Block clustering with Bernoulli mixture models: Comparison of different approaches," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3233-3245, February.
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