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A Novel Framework for Discovering Robust Cluster Results

  • Hye-Sung Yoon
  • Sang-Ho Lee
  • Sung-Bum Cho
  • Ju Han Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4265)

Abstract

We propose a novel method, called heterogeneous clustering ensemble (HCE), to generate robust clustering results that combine multiple partitions (clusters) derived from various clustering algorithms. The proposed method combines partitions of various clustering algorithms by means of newly-proposed the selection and the crossover operation of the genetic algorithm (GA) during the evolutionary process.

Keywords

Genetic Algorithm Cluster Algorithm Chronic Fatigue Syndrome Cluster Result Crossover Operation 
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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References

  1. 1.
    Greene, D., Tsymbal, A., Bolshakova, N., Cunningham, P.: Ensemble clustering in medical diagnostics. In: Proceedings of the 17th IEEE Symposium on Computer-Based Medical Systems, pp. 576–581 (2004)Google Scholar
  2. 2.
    Yoon, H.-S., Ahn, S.-Y., Lee, S.-H., Cho, S.-B., Kim, J.H.: Heterogeneous Clustering Ensemble Method for Combining Different Cluster Results. In: Li, J., Yang, Q., Tan, A.-H. (eds.) BioDM 2006. LNCS (LNBI), vol. 3916, pp. 82–92. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Jouve, P.E., Nicoloyannis, N.: A new method for combining partitions, applications for distributed clustering. In: Proceedings of the International Workshop on Parallel and Distributed Machine Learning and Data Mining (2003)Google Scholar
  4. 4.
    Qiu, P., Wang, Z.J., Liu, K.J.: Ensemble dependence model for classification and prediction of cancer and normal gene expression data. Bioinformatics 21, 3114–3121 (2005)CrossRefGoogle Scholar
  5. 5.
    Whistler, T., Unger, E.R., Nisenbaum, R., Vernon, S.D.: Integration of gene expression, clinical, and epidemiologic data to characterize Chronic Fatigue Syndrome. Journal of Translational Medicine 1 (2003)Google Scholar
  6. 6.
    Xiaohua, H.: Integration of cluster ensemble and text summarization for gene. In: Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering, pp. 251–258 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hye-Sung Yoon
    • 1
  • Sang-Ho Lee
    • 1
  • Sung-Bum Cho
    • 2
  • Ju Han Kim
    • 2
  1. 1.Department of Computer Science and EngineeringEwha Womans UniversitySeoulKorea
  2. 2.Seoul National University Biomedical Informatics (SNUBI)Seoul National University College of MedicineSeoulKorea

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