Supporting bi-cluster interpretation in 0/1 data by means of local patterns

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 -bi-sets (i.e., an extension of formal concepts towards faulttolerance). We introduce the idea of characterizing qu...

• Type: Journal
• Year: 2006
• Where: IDA
• Authors: Ruggero G. Pensa, Céline Robardet, Jean-François Boulicaut
• Keywords: Formal Concepts, IDA 2006, Information Technology, Large Boolean Data, Local Patterns