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Integration Analysis of Diverse Genomic Data Using Multi-clustering 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 4345)

Abstract

In modern data mining applications, clustering algorithms are among the most important approaches, because these algorithms group elements in a dataset according to their similarities, and they do not require any class label information. In recent years, various methods for ensemble selection and clustering result combinations have been designed to optimize clustering results. Moreover, conducting data analysis using multiple sources, given the complexity of data objects, is a much more powerful method than evaluating each source separately. Therefore, a new paradigm is required that combines the genome-wide experimental results of multi-source datasets. However, multi-source data analysis is more difficult than single source data analysis. In this paper, we propose a new clustering ensemble approach for multi-source bio-data on complex objects. In addition, we present encouraging clustering results in a real bio-dataset examined using our proposed method.

Keywords

Cluster Algorithm Chronic Fatigue Syndrome Cluster Result Cluster Ensemble Roulette Wheel Selection 
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.
    Alexander, P.T., Behrouz, M.-B., Anil, K.J., William, F.P.: Adaptive clustering ensembles. In: Proceedings of the International Conference on Pattern Recognition, vol. 1, pp. 272–275 (2004)Google Scholar
  2. 2.
    Alexander, S., Joydeep, G.: Cluster ensembles-A knowledge reuse framework for combining partitionings. Journal of Machine Learning 3, 583–617 (2002)Google Scholar
  3. 3.
    Ana, L.N.F., Anil, K.J.: Combining multiple clusterings using evidence accumulation. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 835–850 (2005)CrossRefGoogle Scholar
  4. 4.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification, 2nd edn. Wiley, Chichester (2001)MATHGoogle Scholar
  5. 5.
    Everitt, B.: Cluster analysis. John Wiley and Sons, Inc., Chichester (1993)Google Scholar
  6. 6.
    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
  7. 7.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: A review. ACM Computing Surveys 31 (1999)Google Scholar
  8. 8.
    Kaufman, L., Rosseeuw, P.J.: Finding groups in data: An introduction to cluster analysis. John Wiley and Sons, Inc., Chichester (1990)Google Scholar
  9. 9.
    Larray, T.H.Y., Fu-lai, C., Stephen, C.F.: Using emerging pattern based projected clustering and gene expression data for cancer detection. In: Proceedings of the Asia-Pacific Bioinformatics Conference, vol. 29, pp. 75–87 (2004)Google Scholar
  10. 10.
    Pavlidis, P., Weston, J., Cai, J., Grundy, W.N.: Learning gene functional classifications from multiple data types. Journal of Computational Biology 9, 401–411 (2002)CrossRefGoogle Scholar
  11. 11.
    Qiu, P., Wang, Z.J., Liu, K.J.: Ensemble dependence model for classification and prediction of cancer and normal gene expression data. Bioinformatics and Bioengineering, 251–258 (2004)Google Scholar
  12. 12.
    Theodoridis, S., Koutroumbas, K.: Pattern recognition. Academic Press, London (1999)Google Scholar
  13. 13.
    Xiaohua, H., Illhoi, Y.: Cluster ensemble and its applications in gene expression. In: Proceedings of the Asia-Pacific Bioinformatics Conference, vol. 29, pp. 297–302 (2004)Google Scholar
  14. 14.
    Zhou, Z.-H., Tang, W.: Clustering ensemble. Knowledge-Based Systems (2006)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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