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Heterogeneous Clustering Ensemble Method for Combining Different Cluster Results

  • Hye-Sung Yoon
  • Sun-Young Ahn
  • Sang-Ho Lee
  • Sung-Bum Cho
  • Ju Han Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3916)

Abstract

Biological data set sizes have been growing rapidly with the technological advances that have occurred in bioinformatics. Data mining techniques have been used extensively as approaches to detect interesting patterns in large databases. In bioinformatics, clustering algorithm technique for data mining can be applied to find underlying genetic and biological interactions, without considering prior information from datasets. However, many clustering algorithms are practically available, and different clustering algorithms may generate dissimilar clustering results due to bio-data characteristics and experimental assumptions. In this paper, we propose a novel heterogeneous clustering ensemble scheme that uses a genetic algorithm to generate high quality and robust clustering results with characteristics of bio-data. The proposed method combines results of various clustering algorithms and crossover operation of genetic algorithm, and is founded on the concept of using the evolutionary processes to select the most commonly-inherited characteristics. Our framework proved to be available on real data set and the optimal clustering results generated by means of our proposed method are detailed in this paper. Experimental results demonstrate that the proposed method yields better clustering results than applying a single best clustering algorithm.

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hye-Sung Yoon
    • 1
  • Sun-Young Ahn
    • 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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