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A Unified Approach to Biclustering Based on Formal Concept Analysis and Interval Pattern Structure

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Discovery Science (DS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11828))

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Abstract

In a matrix representing a numerical dataset, a bicluster is a submatrix whose cells exhibit similar behavior. Biclustering is naturally related to Formal Concept Analysis (FCA) where concepts correspond to maximal and closed biclusters in a binary dataset. In this paper, a unified characterization of biclustering algorithms is proposed using FCA and pattern structures, an extension of FCA for dealing with numbers and other complex data. Several types of biclusters – constant-column, constant-row, additive, and multiplicative – and their relation to interval pattern structures is presented.

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Authors and Affiliations

  1. Université de Lorraine, CNRS, Inria, LORIA, 54000, Nancy, France

    Nyoman Juniarta, Miguel Couceiro & Amedeo Napoli

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  1. Nyoman Juniarta
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  2. Miguel Couceiro
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  3. Amedeo Napoli
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Correspondence to Nyoman Juniarta .

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Editors and Affiliations

  1. Jožef Stefan Institute, Ljubljana, Slovenia

    Petra Kralj Novak

  2. Rudjer Bošković Institute, Zagreb, Croatia

    Tomislav Šmuc

  3. Jožef Stefan Institute, Ljubljana, Slovenia

    Sašo Džeroski

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Juniarta, N., Couceiro, M., Napoli, A. (2019). A Unified Approach to Biclustering Based on Formal Concept Analysis and Interval Pattern Structure. In: Kralj Novak, P., Šmuc, T., Džeroski, S. (eds) Discovery Science. DS 2019. Lecture Notes in Computer Science(), vol 11828. Springer, Cham. https://doi.org/10.1007/978-3-030-33778-0_5

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  • DOI: https://doi.org/10.1007/978-3-030-33778-0_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33777-3

  • Online ISBN: 978-3-030-33778-0

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