Analysis of gene expression data: application of quantum-inspired evolutionary algorithm to minimum sum-of-squares clustering

Authors:
Wengang Zhou;Chunguang Zhou;Yanxin Huang;Yan Wang
Affiliations:
College of Computer Science and Technology, Jilin University, Changchun, P.R. China;College of Computer Science and Technology, Jilin University, Changchun, P.R. China;College of Computer Science and Technology, Jilin University, Changchun, P.R. China;College of Computer Science and Technology, Jilin University, Changchun, P.R. China
Venue:
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Year:
2005

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Abstract

Microarray experiments have produced a huge amount of gene expression data. So it becomes necessary to develop effective clustering techniques to extract the fundamental patterns inherent in the data. In this paper, we propose a novel evolutionary algorithm so called quantum-inspired evolutionary algorithm (QEA) for minimum sum-of-squares clustering. We use a new representation form and add an additional mutation operation in QEA. Experiment results show that the proposed algorithm has better global search ability and is superior to some conventional clustering algorithms such as k-means and self-organizing maps.