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Mining numerical data with formal concept analysis and pattern structures

Mehdi Kaytoue 1
1 ORPAILLEUR - Knowledge representation, reasonning
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : The main topic of this thesis addresses the important problem of mining numerical data, and especially gene expression data. These data characterize the behaviour of thousand of genes in various biological situations (time, cell, etc.). A difficult task consists in clustering genes to obtain classes of genes with similar behaviour, supposed to be involved together within a biological process. Accordingly, we are interested in designing and comparing methods in the field of knowledge discovery from biological data. We propose to study how the conceptual classification method called Formal Concept Analysis (FCA) can handle the problem of extracting interesting classes of genes. For this purpose, we have designed and experimented several original methods based on an extension of FCA called pattern structures. Furthermore, we show that these methods can enhance decision making in agronomy and crop sanity in the vast formal domain of information fusion.
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Contributor : Mehdi Kaytoue <>
Submitted on : Wednesday, June 8, 2011 - 5:01:20 PM
Last modification on : Thursday, March 5, 2020 - 4:53:59 PM
Document(s) archivé(s) le : Friday, September 9, 2011 - 12:06:41 PM


  • HAL Id : tel-01746156, version 2



Mehdi Kaytoue. Mining numerical data with formal concept analysis and pattern structures. Computer Science [cs]. Université Henri Poincaré - Nancy 1, 2011. English. ⟨NNT : 2011NAN10015⟩. ⟨tel-01746156v2⟩



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