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Conference Papers Year : 2014

Bicluster enumeration using Formal Concept Analysis

Victor Codocedo
Amedeo Napoli


In this work we introduce a novel technique to enumerate constant row/column value biclusters using formal concept analysis. To achieve this, a numerical data-table (standard input for biclustering al-gorithms) is modelled as a many-valued context where rows represent objects and columns represent attributes. Using equivalence relations de-fined for each single column, we are able to translate the bicluster mining problem in terms of the partition pattern structure framework. We show how biclustering can benefit from the FCA framework through its ro-bust theoretical description and efficient algorithms. Finally, we show how this technique is able to find high quality biclusters (in terms of the mean squared error) more efficiently than a state-of-the-art bicluster algorithm.
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Dates and versions

hal-01095884 , version 1 (16-12-2014)


  • HAL Id : hal-01095884 , version 1


Victor Codocedo, Amedeo Napoli. Bicluster enumeration using Formal Concept Analysis. What formal concept analysis can do for artificial intelligence? (FCA4AI 2014) Workshop at ECAI 2014, Aug 2014, Prague, Czech Republic. ⟨hal-01095884⟩
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