Abstract
With the rapid development of genome projects, clustering of gene expression data is a crucial step in analyzing gene function and relationship of conditions. In this paper, we put forward an estimation of distribution algorithm for fuzzy clustering gene expression data, which combines estimation of distribution algorithms and fuzzy logic. Comparing with sGA, our method can avoid many parameters and can converge quickly. Tests on real data show that EDA converges ten times as fast as sGA does in clustering gene expression data. For clustering accuracy, EDA can get a more reasonable result than sGA does in the worst situations although both methods can get the best results in the best situations.
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© 2006 Springer-Verlag Berlin Heidelberg
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Liu, F., Liu, J., Feng, J., Zhou, H. (2006). Estimation Distribution of Algorithm for Fuzzy Clustering Gene Expression Data. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881223_40
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DOI: https://doi.org/10.1007/11881223_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45907-1
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