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)


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.


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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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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