PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Pensa R. G., Boulicaut J. Numerical data co-clustering via sum-squared residue minimization and user-defined constraint satisfaction. In: SEBD 2008 - The Sixteenth Italian Symposium on Advanced Database Systems (Mondello (PA), Italy, 22-25 giugno 2008). Atti, pp. 279 - 286. SEBD, 2008.
 
 
Abstract
(English)
Co-clustering aims at computing a bi-partition that is a collection of co-clusters: each co-cluster is a group of objects associated to a group of attributes and these associations can support interpretations. We consider constrained co-clustering not only for extended must-link and cannot-link constraints (i.e., both objects and attributes can be involved), but also for interval constraints that enforce properties of coclusters when considering ordered domains. We propose an iterative coclustering algorithm which exploits user-defined constraints while minimizing the sum-squared residues, i.e., an objective function introduced for gene expression data clustering by Cho et al. (2004).
Subject co-clustering
gene expression data analysis
H.2.8 Database Applications


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