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IDA
2005
Springer

From Local Pattern Mining to Relevant Bi-cluster Characterization

9 years 7 months ago
From Local Pattern Mining to Relevant Bi-cluster Characterization
Clustering or bi-clustering techniques have been proved quite useful in many application domains. A weakness of these techniques remains the poor support for grouping characterization. We consider eventually large Boolean data sets which record properties of objects and we assume that a bi-partition is available. We introduce a generic cluster characterization technique which is based on collections of bi-sets (i.e., sets of objects associated to sets of properties) which satisfy some userdefined constraints, and a measure of the accuracy of a given bi-set as a bi-cluster characterization pattern. The method is illustrated on both formal concepts (i.e., “maximal rectangles of true values”) and the new type of δ-bi-sets (i.e., “rectangles of true values with a bounded number of exceptions per column”). The added-value is illustrated on benchmark data and two real data sets which are intrinsically noisy: a medical data about meningitis and Plasmodium falciparum gene expression ...
Ruggero G. Pensa, Jean-François Boulicaut
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where IDA
Authors Ruggero G. Pensa, Jean-François Boulicaut
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