Journal
INFORMATION SCIENCES
Volume 181, Issue 10, Pages 1989-2001Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2010.07.007
Keywords
Formal concept analysis; Conceptual scaling; Numerical data; Pattern structures; Gene expression data
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Funding
- Russian Foundation for Basic Research [08-07-92497-NTsNIL_a]
- Contrat de Plan Etat - Region Lorraine: Modelisation, Information et Systemes Numeriques
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This paper addresses the important problem of efficiently mining numerical data with formal concept analysis (FCA). Classically, the only way to apply FCA is to binarize the data, thanks to a so-called scaling procedure. This may either involve loss of information, or produce large and dense binary data known as hard to process. In the context of gene expression data analysis, we propose and compare two FCA-based methods for mining numerical data and we show that they are equivalent. The first one relies on a particular scaling, encoding all possible intervals of attribute values, and uses standard FCA techniques. The second one relies on pattern structures without a priori transformation, and is shown to be more computationally efficient and to provide more readable results. Experiments with real-world gene expression data are discussed and give a practical basis for the comparison and evaluation of the methods. (C) 2011 Published by Elsevier Inc.
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