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Consensus clustering based on constrained self-organizing map and improved Cop-Kmeans ensemble in intelligent decision support systems. Presenter : Chuang, Kai-Ting Authors : Yan Yang, Wei Tan, Tianrui Li, Da Ruan 2012, KBS. Outlines. Motivation Objectives Methodology

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Outlines

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  1. Consensus clustering based on constrained self-organizing map and improved Cop-Kmeans ensemble in intelligent decision support systems Presenter : Chuang, Kai-TingAuthors : Yan Yang, Wei Tan, Tianrui Li, Da Ruan2012, KBS

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. Motivation • Cop-Kmeans is a K-means variant that incorporates background knowledge in the form of pairwise constraints. • However, there exists a constraint violation in Cop-Kmeans.

  4. Objectives • An improved Cop-Kmeans (ICop-Kmeans) algorithm to solve the constraint violation of Cop-Kmeans. • A new constrained self-organizing map (SOM) to enhancing the performance of ICop-Kmeans.

  5. Methodology-Framework

  6. Methodology

  7. Methodology-Kmeans

  8. Methodology-Cop-Kmeans

  9. Methodology-ICop-Kmeans

  10. Methodology

  11. Methodology

  12. Methodology

  13. Methodology

  14. Methodology

  15. Methodology-COP-SOM

  16. Methodology-COP-SOM Competition layer Input layer

  17. Experiments-dataset

  18. Experiments-Proportion of failure

  19. Experiments-F-measure

  20. Experiments-time performance

  21. Experiments-kinds of order

  22. Experiments-variance

  23. Experiments-average F-measure

  24. Experiments

  25. Conclusions • The proposed methods could effectively overcome disadvantages of Cop-Kmeans, and Icop-Kmeans performs better using the produced order and its performance is further enhanced using the clustering ensemble technique.

  26. Comments • Advantages • The approach is helpful. • Applications • SOM.

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