Biclustering meets triadic concept analysis

  • Mehdi Kaytoue
  • Sergei O. Kuznetsov
  • Juraj Macko
  • Amedeo Napoli


Biclustering numerical data became a popular data-mining task at the beginning of 2000’s, especially for gene expression data analysis and recommender systems. 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, a well-known intractable problem, while no formal framework exists. We introduce important links between biclustering and Formal Concept Analysis (FCA). Indeed, FCA is known to be, among others, a methodology for biclustering binary data. Handling numerical data is not direct, and we argue that Triadic Concept Analysis (TCA), the extension of FCA to ternary relations, provides a powerful mathematical and algorithmic framework for biclustering numerical data. We discuss hence both theoretical and computational aspects on biclustering numerical data with triadic concept analysis. These results also scale to n-dimensional numerical datasets.


Numerical biclustering Similarity relation Formal concept analysis Triadic concept analysis N-ary relations 

Mathematics Subject Classifications (2010)

06 68 


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Mehdi Kaytoue
    • 1
  • Sergei O. Kuznetsov
    • 2
  • Juraj Macko
    • 3
  • Amedeo Napoli
    • 4
  1. 1.Université de Lyon, CNRS, INSA-Lyon, LIRISVilleurbanne CedexFrance
  2. 2.Higher School of Economis (HSE)MoscowRussia
  3. 3.Palacky UniversityOlomoucCzech Republic
  4. 4.Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA)Vandœuvre-lès-NancyFrance

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