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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Towards Constrained Co-clustering in Ordered 0/1 Data Sets
verfasst von : Ruggero G. Pensa, Céline Robardet, Jean-François Boulicaut
Erschienen in: Foundations of Intelligent Systems
Verlag: Springer Berlin Heidelberg
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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.