Numerical Data Co-clustering via Sum-Squared Residue Minimization and User-defined Constraint Satisfaction (extended abstract) - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2008

Numerical Data Co-clustering via Sum-Squared Residue Minimization and User-defined Constraint Satisfaction (extended abstract)

Résumé

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 co-clusters when considering ordered domains. We propose an iterative co-clustering 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).
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Dates et versions

hal-01501227 , version 1 (04-04-2017)

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  • HAL Id : hal-01501227 , version 1

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Ruggero Pensa, Jean-François Boulicaut. Numerical Data Co-clustering via Sum-Squared Residue Minimization and User-defined Constraint Satisfaction (extended abstract). 16th Italian Symposium on Advanced Database Systems SEBD 2008, Jun 2008, Mondello, Italy. pp.279-286. ⟨hal-01501227⟩
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