Towards constrained co-clustering in ordered 0/1 data sets

Authors:
Ruggero G. Pensa;Céline Robardet;Jean-François Boulicaut
Affiliations:
LIRIS CNRS UMR 5205, INSA Lyon, Villeurbanne, France;LIRIS CNRS UMR 5205, INSA Lyon, Villeurbanne, France;LIRIS CNRS UMR 5205, INSA Lyon, Villeurbanne, France
Venue:
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Year:
2006

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Abstract

Within 0/1 data, co-clustering provides a collection of bi-clusters, i.e., linked clusters for both objects and Boolean properties. Beside the classical need for grouping quality optimization, one can also use user-defined constraints to capture subjective interestingness aspects and thus to improve bi-cluster relevancy. We consider the case of 0/1 data where at least one dimension is ordered, e.g., objects denotes time points, and we introduce co-clustering constrained by interval constraints. Exploiting such constraints during the intrinsically heuristic clustering process is challenging. We propose one major step in this direction where bi-clusters are computed from collections of local patterns. We provide an experimental validation on two temporal gene expression data sets.