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Issue title: Selected papers from IDA2005, Madrid, Spain
Article type: Research Article
Authors: Pensa, Ruggero G.; * | Robardet, Céline | Boulicaut, Jean-François
Affiliations: INSA Lyon, LIRIS CNRS UMR 5205, F-69621 Villeurbanne cedex, France
Correspondence: [*] Corresponding author. Tel.: +33 4 72 43 70 24; Fax: +33 4 72 43 87 13; E-mail: ruggero.pensa@insa-lyon.fr.
Abstract: Clustering or co-clustering techniques have been proved useful in many application domains. A weakness of these techniques remains the poor support for grouping characterization. As a result, interpreting clustering results and discovering knowledge from them can be quite hard. We consider potentially large Boolean data sets which record properties of objects and we assume the availability of a bi-partition which has to be characterized by means of a symbolic description. Our generic approach exploits collections of local patterns which satisfy some user-defined constraints in the data, and a measure of the accuracy of a given local pattern as a bi-cluster characterization pattern. We consider local patterns which are bi-sets, i.e., sets of objects associated to sets of properties. Two concrete examples are formal concepts (i.e., associated closed sets) and the so-called δ-bi-sets (i.e., an extension of formal concepts towards fault-tolerance). We introduce the idea of characterizing query which can be used by experts to support knowledge discovery from bi-partitions thanks to available local patterns. The added-value is illustrated on benchmark data and three real data sets: a medical data set and two gene expression data sets.
Keywords: Co-clustering, characterization, closed sets, fault-tolerant formal concept
DOI: 10.3233/IDA-2006-10505
Journal: Intelligent Data Analysis, vol. 10, no. 5, pp. 457-472, 2006
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