Evolutionary Clustering Algorithm with Knowledge-Based Evaluation for Fuzzy Cluster Analysis of Gene Expression Profiles

  • Han-Saem Park
  • Sung-Bae Cho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3776)


Clustering method, which groups thousands of genes by their similarities of expression levels, has been used for identifying unknown functions of genes. Fuzzy clustering method that is one category of clustering assigns one sample to multiple groups according to their membership degrees. It is more appropriate than hard clustering algorithms for analyzing gene expression profiles since single gene might involve multiple genetic functions. However, general clustering methods have problems that they are sensitive to initialization and can be trapped into local optima. To solve the problems, we propose an evolutionary fuzzy clustering algorithm with knowledge-based evaluation. It uses a genetic algorithm for clustering and prior knowledge of data for evaluation. Yeast cell-cycle dataset has been used for experiments to show the usefulness of the proposed method.


Fuzzy Cluster Membership Degree Cluster Partition Analyze Gene Expression Profile Fuzzy Cluster Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Han-Saem Park
    • 1
  • Sung-Bae Cho
    • 1
  1. 1.Department of Computer ScienceYonsei University, Biometrics Engineering Research CenterSeoulKorea

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