Towards Fault-Tolerant Formal Concept Analysis

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Towards Fault-Tolerant Formal Concept Analysis
Given Boolean data sets which record properties of objects, Formal Concept Analysis is a well-known approach for knowledge discovery. Recent application domains, e.g., for very large data sets, have motivated new algorithms which can perform constraint-based mining of formal concepts (i.e., closed sets on both dimensions which are associated by the Galois connection and satisfy some user-defined constraints). In this paper, we consider a major limit of these approaches when considering noisy data sets. This is indeed the case of Boolean gene expression data analysis where objects denote biological experiments and attributes denote gene expression properties. In this type of intrinsically noisy data, the Galois association is so strong that the number of extracted formal concepts explodes. We formalize the computation of the so-called δ-bisets as an alternative for capturing strong associations between sets of objects and sets of properties. Based on a previous work on approximate con...
Ruggero G. Pensa, Jean-François Boulicaut
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where AIIA
Authors Ruggero G. Pensa, Jean-François Boulicaut
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