DMCA
Constrained Co-clustering of Gene Expression Data (2008)
| Citations: | 13 - 3 self |
BibTeX
@MISC{Pensa08constrainedco-clustering,
author = {Ruggero G. Pensa and et al.},
title = { Constrained Co-clustering of Gene Expression Data},
year = {2008}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
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 ... 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 data clustering by Cho et al. (2004). We illustrate the added value of our approach in two applications on gene expression data.
Keyphrases
gene expression data cannot-link constraint domain knowledge sum-squared residue added value many application mono-dimensional clustering interval constraint partition relevancy gene expression data clustering co-clustering aim iterative coclustering algorithm mono-dimensional case expert interpretation so-called must-link objective function user-defined constraint
