Constrained Co-clustering of Gene Expression Data

In many applications, the expert interpretation of coclustering is easier than for mono-dimensional clustering. 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. Many constrained clustering algorithms have been proposed to exploit the domain knowledge and to improve partition relevancy in the mono-dimensional case (e.g., using the so-called must-link and cannot-link constraints). 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 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 dat...

• Type: Conference
• Year: 2008
• Where: SDM
• Authors: Ruggero G. Pensa, Jean-François Boulicaut
• Keywords: Cannot-link Constraints, Constrained Clustering Algorithms, Data Mining, Gene Expression Data, SDM 2008