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Multi-objective evolutionary algorithm for mining 3D clusters in gene-sample-time microarray data

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4 Author(s)
Junwan Liu ; Sch. of Comput., Nat. Univ. of Defence Technol., Changsha ; Zhoujun Li ; Xiaohua Hu ; Yiming Chen

Latest microarray technique can measure the expression levels of a set of genes under a set of samples during a series of time points, and generates new datasets which are called gene-sample-time (simply GST) microarray data. Mining three-dimensional (3D) clusters from GST datasets is important in bioinformatics research and biomedical applications. Several objectives in conflict with each other have to be optimized simultaneously during mining 3D clusters, so multi-objective modeling is suitable for solving 3D clustering. This paper proposes a novel multi-objective evolutionary 3D clustering algorithm to mine 3D cluster in 3D microarray data. Experimental results on real dataset show that our approach can find significant 3D clusters of high quality.

Published in:

Granular Computing, 2008. GrC 2008. IEEE International Conference on

Date of Conference:

26-28 Aug. 2008