Estimation Distribution of Algorithm for Fuzzy Clustering Gene Expression Data

  • Feng Liu
  • Juan Liu
  • Jing Feng
  • Huaibei Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4222)


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.


Fuzzy Logic Convergence Speed Fuzzy Cluster Acute Lymphoblastic Leukaemia Gray Code 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Feng Liu
    • 1
  • Juan Liu
    • 1
  • Jing Feng
    • 1
  • Huaibei Zhou
    • 2
  1. 1.Computer School of Wuhan UniversityWuhan UniversityWuhanChina
  2. 2.International School of SoftwareWuhan UniversityWuhanChina

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