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Co-clustering Numerical Data Under User-defined Constraints by Ruggero G. Pensa; Jean-Francois Boulicaut; Francesca Cordero; Maurizio Atzori is a Computer Science article available to read on EtoBox.

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## Abstract In the generic setting of objects × attributes matrix data analysis, co‐clustering appears as an interesting unsupervised data mining method. A co‐clustering task provides a bi‐partition made of co‐clusters: each co‐cluster is a group of objects associated to a group of attributes and these associations can support expert interpretations. Many constrained clustering algorithms have been proposed to exploit the domain knowledge and to improve partition relevancy in the mono‐dimensional clustering case (e.g. using the must‐link and cannot‐link constraints on one of the two dimensions). Here, 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 describe an iterative co‐clustering algorithm which exploits user‐defined constraints while minimizing a given objective function. Thanks to a generic setting, we emphasize that different objective functions can be used. The added value of our approach is demonstrated on both synthetic and real data. Among others, several experim

Who reads Co-clustering Numerical Data Under User-defined Constraints?

Author

Ruggero G. Pensa; Jean-Francois Boulicaut; Francesca Cordero; Maurizio Atzori

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); Wiley Subscription Services; Wiley (ISSN 1932-1864)

Published

2009

Language

EN

Field

Computer Science (Physical Sciences)