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A robust adaptive clustering analysis method for automatic identification of clusters

Presenter : Bo- Sheng Wang Authors : P.Y. Mok *, H.Q. Huang, Y.L. Kwok, J.S. Au PR, 2012. A robust adaptive clustering analysis method for automatic identification of clusters. Outlines. Motivation Objectives Methodology Experiments Compary Conclusions Comments. Motivation.

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A robust adaptive clustering analysis method for automatic identification of clusters

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  1. Presenter : Bo-Sheng Wang Authors : P.Y. Mok*, H.Q. Huang, Y.L. Kwok, J.S. Au PR, 2012 A robust adaptive clustering analysis method for automatic identification of clusters

  2. Outlines Motivation Objectives Methodology Experiments Compary Conclusions Comments

  3. Motivation Correct cluster numbers do not guarantee that a data set can be properly partitioned in the desired way.

  4. Objectives The objective of this paper is to propose a robust and adaptive clustering analysis method. 1.Produces reliable clustering results 2.Identifies the desired cluster number.

  5. Methodology-Fuzzy C-mean(FCM)

  6. Methodology-Fuzzy C-mean(Example)

  7. Methodology-Fuzzy C-mean(Example)

  8. Methodology-Fuzzy C-mean(Example)

  9. Methodology-Fuzzy C-mean(Example)

  10. Mothodology-RAC-FCM

  11. Mothodology-RAC-FCM

  12. Mothodology-RAC-FCM

  13. Mothodology-RAC-FCM

  14. Mothodology-Adaptive implementation

  15. Experiments-K-mean KM

  16. Experiments-K-mean+RAC-FCM

  17. Mothodology-Application

  18. Experiments When the distribution of cluster number is not stable enough to give the desired number. Increasing the upper bound of cluster number can.

  19. Experiments

  20. Experiments How to verification the proposed k parameter?

  21. Experiments This paper use the three widely data sets including the Iris data set, Breast Cancer Wisconsin (Diagnostic) data set and Wine data set. Step: 1.Verified the distribution stability of the cluster number 2.Compared to different cluster validity index methods.

  22. Experiments -Iris Data Set

  23. Experiments -Breast Cancer Wisconsin (Diagnostic) data set

  24. Experiments -Wine data set

  25. Experiments -Compary different Data Set

  26. Compary-Comparison with the spectral clustering method

  27. Compary-Comparison with cluster ensembles

  28. Conclusions This paper proposes method no cluster number is needed to define. The method is not only robust but also adaptive. The method not only identifies the desired cluster number but also ensures reliable clustering results.

  29. Comments • Advantages • We can obtain optimum Result use this method in cluster analysis. • Disadvantage • This method is very take the time because of a program. • Applications • Cluster Analysis

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