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FCM-Based Model Selection Algorithms for Determining the Number of Clusters

FCM-Based Model Selection Algorithms for Determining the Number of Clusters. Haojun Sun,ShengruiWang *, Qingshan Jiang Received 16 December 2002; received in revised form 29 March 2004; accepted 29 March 2004. Presenter Chia-Cheng Chen. Outline. Introduction Basic algorithm

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FCM-Based Model Selection Algorithms for Determining the Number of Clusters

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  1. FCM-Based Model Selection Algorithms for Determining theNumber of Clusters HaojunSun,ShengruiWang*,Qingshan Jiang Received 16 December 2002; received in revised form 29 March 2004; accepted 29 March 2004 Presenter Chia-Cheng Chen

  2. Outline • Introduction • Basic algorithm • A new validity index • Experimental results • Conclusion and perspectives

  3. Introduction • Clustering is a process for grouping a set of objects into classes or clusters so that the objects within a cluster have high similarity. • Because of its concept of fuzzy membership, FCM is able to deal more effectively with outliers and to perform membership grading, which is very important in practice.

  4. Basic algorithm • FCM algorithm

  5. Basic algorithm(Cont.)

  6. Basic algorithm(Cont.) • FCM-based model selection algorithm

  7. Basic algorithm(Cont.) • FBSA: FCM-Based Splitting Algorithm

  8. Basic algorithm(Cont.) • Function S(i)

  9. A new validity index • A new validity index

  10. A new validity index(Cont.)

  11. Experimental results • DataSet1 • IRIS data • This is a biometric data set consisting of 150 measurements belonging to three flower varieties • DataSet2 • Mixture of Gaussian distributions • 50 data vectors in each of the 5ve clusters • DataSet3 • Mixture of Gaussian distributions • 500 data vectors

  12. Experimental results(Cont.)

  13. Experimental results(Cont.)

  14. Experimental results(Cont.)

  15. Experimental results(Cont.)

  16. Experimental results(Cont.)

  17. Experimental results(Cont.)

  18. Conclusion and perspectives • The major contributions of this paper are an improved FCM-based algorithm for determining the number of clusters and a new index for validating clustering results. • Use of the new algorithm to deal with the dimension reduction problem is another promising avenue.

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