Mining Biclusters of Similar Values with Triadic Concept Analysis

Abstract : Biclustering numerical data became a popular data-mining task in the beginning of 2000's, especially for analysing gene expression data. A bicluster reflects a strong association between a subset of objects and a subset of attributes in a numerical object/attribute data-table. So called biclusters of similar values can be thought as maximal sub-tables with close values. Only few methods address a complete, correct and non redundant enumeration of such patterns, which is a well-known intractable problem, while no formal framework exists. In this paper, we introduce important links between biclustering and formal concept analysis. More specifically, we originally show that Triadic Concept Analysis (TCA), provides a nice mathematical framework for biclustering. Interestingly, existing algorithms of TCA, that usually apply on binary data, can be used (directly or with slight modifications) after a preprocessing step for extracting maximal biclusters of similar values.
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Communication dans un congrès
Amedeo Napoli and Vilem Vychodil. The Eighth International Conference on Concept Lattices and their Applications - CLA 2011, Oct 2011, Nancy, France. 2011
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https://hal.inria.fr/hal-00640873
Contributeur : Mehdi Kaytoue <>
Soumis le : lundi 14 novembre 2011 - 13:41:22
Dernière modification le : jeudi 11 janvier 2018 - 06:19:54
Document(s) archivé(s) le : vendredi 16 novembre 2012 - 10:51:26

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  • HAL Id : hal-00640873, version 1
  • ARXIV : 1111.3270

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Mehdi Kaytoue, Sergei O. Kuznetsov, Juraj Macko, Wagner Meira, Amedeo Napoli. Mining Biclusters of Similar Values with Triadic Concept Analysis. Amedeo Napoli and Vilem Vychodil. The Eighth International Conference on Concept Lattices and their Applications - CLA 2011, Oct 2011, Nancy, France. 2011. 〈hal-00640873〉

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