CEUR Workshop Proceedings

Mehdi Kaytoue, Victor Codocedo, Jaume Baixieres, Amedeo Napoli

Research output: Contribution to conferencePaper

1 Citation (Scopus)


Biclustering numerical data tables consists in detecting particular and strong associations between both subsets of objects and attributes. Such biclusters are interesting since they model the data as local patterns. Whereas there exists several definitions of biclusters, depending on the constraints they should respect, we focus in this paper on biclusters of similar values on columns. There are several ad hoc methods for mining such biclusters in the literature. We focus here on two aspects: genericity and efficiency. We show that Formal Concept Analysis provides a mathematical framework to characterize them in several ways, but also to compute them with existing and efficient algorithms. The proposed methods, which rely on pattern structures and triadic concept analysis, are experimented and compared on two different datasets.
Original languageEnglish
Number of pages12
Publication statusPublished - 1 Jan 2014
Externally publishedYes
Eventconference -
Duration: 1 Jan 2014 → …


Period1/01/14 → …

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    Kaytoue, M., Codocedo, V., Baixieres, J., & Napoli, A. (2014). CEUR Workshop Proceedings. 243-254. Paper presented at conference, . https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84961340413&origin=inward