Constrained clustering for gene expression data mining

Authors:
Vincent S. Tseng;Lien-Chin Chen;Ching-Pin Kao
Affiliations:
Dept. of Computer Science and Information Engineering, National Cheng Kung University, Taiwan, R.O.C.;Dept. of Computer Science and Information Engineering, National Cheng Kung University, Taiwan, R.O.C.;Dept. of Computer Science and Information Engineering, National Cheng Kung University, Taiwan, R.O.C.
Venue:
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2008

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Abstract

Constrained clustering algorithms have the advantage that domaindependent constraints can be incorporated in clustering so as to achieve better clustering results. However, the existing constrained clustering algorithms are mostly k-means like methods, which may only deal with distance-based similarity measures. In this paper, we propose a constrained hierarchical clustering method, called Correlational-Constrained Complete Link (C-CCL), for gene expression analysis with the consideration of gene-pair constraints, while using correlation coefficients as the similarity measure. C-CCL was evaluated for the performance with the correlational version of COP-k-Means (C-CKM) method on a real yeast dataset. We evaluate both clustering methods with two validation measures and the results show that C-CCL outperforms C-CKM substantially in clustering quality