Automatically Determining the Number of Clusters in Unlabeled Data Sets

1. Automatically Determining the Number ofClusters in Unlabeled Data Sets Presenter : Lin, Shu-Han Authors : Liang Wang, Christopher Leckie, KotagiriRamamohanarao, and James Bezdek IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING(TKD), 2009

2. Outline • Motivation • Objective • Methodology • Experiments • Conclusion • Comments

3. Motivation “reordered dissimilarity image” (RDI) Howtoautomaticallyestimatethenumberofclustersinunlabeleddataset?

4. Objectives ExtractDarkBlock 4

5. Methodology– VAT VAT 5

6. Methodology– VAT VAT 6

7. Methodology– DBE 1 2 3 4 7

8. Methodology– DBE1.Dissimilaritytransformationandimagesegmentation f(t) Graythreshfunction(Matlab):σ 8 after before

9. Methodology– DBE2. Directionalmorphologicalfilteringofthebinaryimage a=2% a=1% Symmetric: along horizontal and vertical directions Linear: along the same direction 9

10. Methodology– DBE3. Distancetransformanddiagonalprojectionoffilteredimage Nearest non-zero pixel 10

11. Methodology– DBE4. Detection of major peaks and valleys in the projectionsignal Smooth(parameter:a) Major“peaks/valleys”(parameter:a) 11

12. Experiments – Syntheticdatasets 12

13. Experiments– ComparewithCCE 13

14. Experiments – ComparewithCCE Syntheticdatasets Realdatasets 14

15. Conclusions • The most method prefer “larger” rather than “smaller” clusters • The DBE • (Nearly) Automatically estimating the number of clusters • Just one easy-to-set parameter: a

16. Comments • Advantage • An visual assessment of cluster tendency (VAT) • Combine the cluster analysis problem with the image processing tech. • Drawback • … • Application • …