1. Evaluation of Clustering Techniques on DMOZ Data  Alper Rifat Uluçınar  Rıfat Özcan  Mustafa Canım

2. Outline  What is DMOZ and why do we use it?  What is our aim? Evaluation of partitioning clustering algorithms Evaluation of hierarchical clustering algorithms  Conclusion

3. What is DMOZ and why do we use it?  www.dmoz.org www.dmoz.org  Another name for ODP, Open Directory Project  The largest human edited directory on the Internet  5,300,000 sites  72,000 editors  590,000 categories

6. What is our aim?  Evaluating cluster algorithms is not easy 1)We will use DMOZ as reference point (ideal cluster structure) 2)Run our own cluster algorithms on same data 3)Finally compare results.

7. All DMOZ documents (websites) Applying Clustering Algorithms such as C3M, K Means etc. Human Evaluation DMOZ Clusters?? ?

8. A) Evaluation of Partitioning Clustering Algorithms  20,000 documents from DMOZ  flat partitioned data (214 folders)  We applied html parsing, stemming, stop word list elimination  We will apply two clustering algorithms : C3M K-Means

9. Before applying html parsing, stemming, stop word list elimination

10. After applying html parsing, stemming, stop word list elimination

11. 20,000 DMOZ documents Applying C3M Human Evaluation 214 Clusters642 Clusters

12. 214 Clusters642 Clusters How to compare DMOZ Clusters and C3M clusters ? C3M ClustersDMOZ Clusters Answer: Corrected Rand

13. Validation of Partitioning Clustering  Comparison of two clustering structures N documents Clustering structure 1:  R clusters Clustering structure 2:  C clusters  Metrics [1]: Rand Index Jaccard Coefficient Corrected Rand Coefficient

14. Validation of Partitioning Clustering ….. d1,d2 ….. d1,d2 Type I, Frequency: a ….. d1,d2 ….. d2 d1 Type II, Frequency: b ….. d2 d1 ….. d1,d2 Type III, Frequency: c ….. d2 d1 ….. d2 d1 Type IV, Frequency: d

15. Validation of Partitioning Clustering  Rand Index = (a+d) / (a+b+c+d)  Jaccard Coefficient = a / (a+b+c)  Corrected Rand Coefficient Accounts for randomness Normalize rand index so that 0 when the partitions are selected by chance and 1 when a perfect match achieved. CR = (R – E(R)) / (1 – E(R))

16. Validation of Partitioning Clustering  Example: Docs: d 1, d 2, d 3, d 4, d 5, d 6 Clustering Structure 1:  C1: d 1, d 2, d 3  C2: d 4, d 5, d 6 Clustering Structure 2:  D1: d 1, d 2  D2: d 3, d 4  D3: d 5, d 6

17. Validation of Partitioning Clustering  Contingency Table: D1D2D3 C12103 C20123 2226 a : (d1, d2), (d5, d6) b : (d1, d3), (d2, d3), (d4, d5), (d4, d6) c : (d3, d4) d : remaining 8 pairs (15-7) Rand Index = (2+8)/15 = 0.66 Jaccard Coeff. = 2/(2+4+1) = 0.29 Corrected Rand = 0.24

18. Results  Results: Low corrected rand and jaccard values  ~=0.01 Rand index ~= 0.77  Possible Reasons: Noise in the data  Ex: 300 Document Not Found pages. Problem is difficult: Ex: Homepages category.

19. B) Evaluation of Hierarchical Clustering Algorithms  Obtain a partitioning of DMOZ Determine a depth (experiment?) Collect documents of higher (or equal) depth at that level Documents of lower depths? Ignore them…

20. Hierarchical Clustering: Steps  Obtain the hierarchical clusters using: Single Linkage Average Linkage Complete Linkage  Obtain a partitioning on the hierarchical cluster…

21. Hierarchical Clustering: Steps  One way, treat DMOZ clusters as “queries”: For each selected cluster of DMOZ  Find the number of “target clusters” on computerized partitioning Take the average See if N t < N tr  If not, either choice of partitioning or hierarchical clustering did not perform well…

22. Hierarchical Clustering: Steps  Another way: Compare the two partitions using an index, i.e. C-RAND…

23. Choice of Partition: Outline  Obtain the dendrogram Single linkage Complete linkage Group average linkage Ward’s methods

24. Choice of Partition: Outline  How to convert a hierarchical cluster structure into a partition? Visually inspect the dendrogram? Use tools from statistics?

25. Choice of Partition: Inconsistency Coefficient  At each fusion level: Calculate the “inconsistency coefficient” Utilize statistics from the previous fusion levels  Choose the fusion level for which inconsistency coefficient is at maximum.

26. Choice of Partition: Inconsistency Coefficient  Inconsistency coefficient (I.C.) at fusion level i:

27. Choice of Partition: I.C. Hands on, Objects  Plot of the objects Distance measure: Euclidean Distance

28. Choice of Partition: I.C. Hands on, Single Linkage

29. Choice of Partition: I.C. Single Linkage Results Level 1  0 Level 2  0 Level 3  0 Level 4  0 Level 5  0 Level 6  1.1323 Level 7  0.6434 => Cut the dendrogram at a height between level 5 & 6

30. Choice of Partition: I.C. Single Linkage Results

31. Choice of Partition: I.C. Hands on, Average Linkage

32. Choice of Partition: I.C. Average Linkage Results Level 1  0 Level 2  0 Level 3  0.7071 Level 4  0 Level 5  0.7071 Level 6  1.0819 Level 7  0.9467 => Cut the dendrogram at a height between level 5 & 6

33. Choice of Partition: I.C. Hands on, Complete Linkage

34. Choice of Partition: I.C. Complete Linkage Results Level 1  0 Level 2  0 Level 3  0.7071 Level 4  0 Level 5  0.7071 Level 6  1.0340 Level 7  1.0116 => Cut the dendrogram at a height between level 5 & 6

35. Conclusion  Our aim is to evaluate clustering techniques on DMOZ Data.  Analysis on partitioning & hierarchical clustering algorithms.  If the experiments are succesfull we will apply same experiments on larger DMOZ data after we download it.  Else We will try other methodologies to improve our experiment results.

36. References  [1] A. K. Jain and R. C. Dubes. Algorithms for Clustering Data. Prentice Hall, 1988.  [2] Korenius T., Laurikkala J., Juhola M., Jarvelin K. Hierarchical clustering of a Finnish newspaper article collection with graded relevance assessments. Information Retrieval, 9(1). Kluwer Academic Publishers, 2006.  www.dmoz.org www.dmoz.org