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.
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Zhou, W., Zhou, C., Huang, Y., Wang, Y. (2005). Analysis of Gene Expression Data: Application of Quantum-Inspired Evolutionary Algorithm to Minimum Sum-of-Squares Clustering. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548706_40
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DOI: https://doi.org/10.1007/11548706_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28660-8
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