1. Intelligent Database Systems Lab Presenter: WU, JHEN-WEI Authors: Olatz Arbelaitz, Ibai Gurrutxaga, Javier Muguerza, Jesús M. Pérez and Iñigo Perona 2013. PR. An extensive comparative study of cluster validity indices

2. Intelligent Database Systems Lab Outlines Motivation Objectives Cluster validity indices Experimental setup Results Conclusions Comments 2

3. Intelligent Database Systems Lab Motivation Many indices have been proposed, there is no recent extensive comparative study of their performance. 3

4. Intelligent Database Systems Lab Objectives To compare 30 cluster validity indices in many different environments with different characteristics. To build a guideline for selecting the most suitable index for each possible application. 4

5. Intelligent Database Systems Lab Cluster validity indices Dunn index (D ↑) 5 Calinski-Harabasz (CH ↑)

6. Intelligent Database Systems Lab Cluster validity indices Gamma index (G ↓) 6 C-Index (CI ↓)

7. Intelligent Database Systems Lab Cluster validity indices 7 Davies-Bouldin index (DB ↓) Silhouette index (Sil ↑)

8. Intelligent Database Systems Lab Cluster validity indices 8 Graph theory based Dunn and Davies-Bouldin variations (D MST ↑, D RNG ↑, D GG ↑, DB MST ↓, DB RNG ↓, DB GG ↓) Generalized Dunn indices (gD31 ↑, gD41 ↑, gD51 ↑, gD33 ↑, gD43 ↑, gD53 ↑)

9. Intelligent Database Systems Lab Cluster validity indices 9 S_Dbw index (SDbw ↓) CS index (CS ↓) Davies-Bouldin* (DB* ↓)

10. Intelligent Database Systems Lab Cluster validity indices 10 Sym-index (Sym ↑) Score function (SF ↑)

11. Intelligent Database Systems Lab Cluster validity indices 11 Point Symmetry-Distance (SymDB ↓, SymD ↑, Sym33 ↑) SymDB SymD Sym33

12. Intelligent Database Systems Lab 12 Cluster validity indices COP index (COP ↓) Negentropy increment (NI ↓)

13. Intelligent Database Systems Lab 13 Cluster validity indices SV-Index (SV ↑) OS-Index (OS ↑)

14. Intelligent Database Systems Lab Experimental setup 14

15. Intelligent Database Systems Lab Results 15

16. Intelligent Database Systems Lab 16 Results - Synthetic datasets

17. Intelligent Database Systems Lab Results – Real datasets 17

18. Intelligent Database Systems Lab 18 Results – Statistical tests

19. Intelligent Database Systems Lab Conclusions Some SVIs appear to be more suitable for certain configurations. The overall trend never changed dramatically when we focused on a particular factor. The results for real and synthetic datasets are qualitatively similar. 19

20. Intelligent Database Systems Lab Comments Contributions – Present the results of the most extensive CVI comparison ever carried out. – This comparison is the first extensive CVI comparison with the methodological correction. Applications – Build a guideline for selecting the most suitable index for each possible application. 20