1. 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 1

2. Outlines Motivation Objectives Methodology Experiments Compary Conclusions Comments 2

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

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. 4

5. Methodology- Fuzzy C-mean(FCM) 5

6. Methodology- Fuzzy C-mean(Example) 6

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10. Mothodology- RAC-FCM 10

11. Mothodology- RAC-FCM 11

12. Mothodology- RAC-FCM 12

13. Mothodology- RAC-FCM 13

14. Mothodology- Adaptive implementation 14

15. Experiments- K-mean 15 KM

16. Experiments- K-mean+RAC-FCM 16

17. Mothodology- Application 17

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. 18

19. Experiments 19

20. Experiments 20

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. 21

22. Experiments - Iris Data Set 22

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

24. Experiments - Wine data set 24

25. Experiments - Compary different Data Set 25

26. Compary- Comparison with the spectral clustering method 26 RAC-FCMSpectral Clustering Method WIN

27. Compary- Comparison with cluster ensembles 27

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. 28

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 29