Constrained Co-clustering of Gene Expression Data

In many applications, the expert interpretation of co-clustering is easier than for mono-dimensional clustering. Co-clustering aims at computing a bi-partition that is a col-lection 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 algo-rithms 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 con-straints). 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 in-terval 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 objec-tive function introduced for gene expression data clustering by Cho et al. (2004). We illustrate the added value of our approach in two applications on gene expression data.