期刊
INFORMATION SCIENCES
卷 181, 期 10, 页码 1989-2001出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2010.07.007
关键词
Formal concept analysis; Conceptual scaling; Numerical data; Pattern structures; Gene expression data
资金
- Russian Foundation for Basic Research [08-07-92497-NTsNIL_a]
- Contrat de Plan Etat - Region Lorraine: Modelisation, Information et Systemes Numeriques
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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