From local pattern mining to relevant bi-cluster characterization - Archive ouverte HAL
Communication Dans Un Congrès Année : 2005

From local pattern mining to relevant bi-cluster characterization

Résumé

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 user-defined 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 delta-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 data.

Dates et versions

hal-01596128 , version 1 (27-09-2017)

Identifiants

Citer

Ruggero Pensa, Jean-François Boulicaut. From local pattern mining to relevant bi-cluster characterization. 6th International Symposium on Intelligent Data Analysis, IDA 2005, Sep 2005, Madrid, Spain. pp.293-304, ⟨10.1007/11552253_27⟩. ⟨hal-01596128⟩
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