1. Semi-Supervised Clustering and its Application to Text Clustering and Record Linkage Raymond J. Mooney Sugato Basu Mikhail Bilenko Arindam Banerjee

2. Supervised Classification Example . . . .

3. Supervised Classification Example . . . . . . . . . . . . . . . . . . . .

4. Supervised Classification Example . . . . . . . . . . . . . . . . . . . .

5. Unsupervised Clustering Example . . . . . . . . . . . . . . . . . . . .

6. Unsupervised Clustering Example . . . . . . . . . . . . . . . . . . . .

8. Semi-Supervised Classification Example . . . . . . . . . . . . . . . . . . . .

9. Semi-Supervised Classification Example . . . . . . . . . . . . . . . . . . . .

11. Semi-Supervised Clustering Example . . . . . . . . . . . . . . . . . . . .

12. Semi-Supervised Clustering Example . . . . . . . . . . . . . . . . . . . .

13. Second Semi-Supervised Clustering Example . . . . . . . . . . . . . . . . . . . .

14. Second Semi-Supervised Clustering Example . . . . . . . . . . . . . . . . . . . .

19. K Means Example

20. K Means Example Randomly Initialize Means x x

21. K Means Example Assign Points to Clusters x x

22. K Means Example Re-estimate Means x x

23. K Means Example Re-assign Points to Clusters x x

24. K Means Example Re-estimate Means x x

25. K Means Example Re-assign Points to Clusters x x

26. K Means Example Re-estimate Means and Converge x x

28. Semi-Supervised K Means Example

29. Semi-Supervised K Means Example Initialize Means Using Labeled Data x x

30. Semi-Supervised K Means Example Assign Points to Clusters x x

31. Semi-Supervised K Means Example Re-estimate Means and Converge x x

33. Semi-Supervised Clustering Example Similarity Based

34. Semi-Supervised Clustering Example Distances Transformed by Learned Metric

35. Semi-Supervised Clustering Example Clustering Result with Trained Metric

51. Record Linkage Examples Author Title Venue Address Year Name Address City Cusine San Mateo, CA. Advances in Neural Information Processing Systems Information, prediction, and query by committee Freund, Y., Seung, H.S., Shamir, E. & Tishby, N. 1993 San Mateo, CA Advances in Neural Information Processing System Information, prediction, and query by committee Yoav Freund, H. Sebastian Seung, Eli Shamir, and Naftali Tishby Delis New York City 156 Second Ave. Second Avenue Deli Delicatessen New York 156 2nd Ave. at 10th Second Avenue Deli

59. MARLIN Record Linkage Framework … … … … … Trainable similarity metrics Trainable duplicate detector A.Field1 B.Field1 A.Field n B.Field n A.Field2 B.Field2 m1 m k m1 m k m1 m k

61. Record Pair Classification Example

64. Mailing List Name Field Results

65. Cora Title Field Results

66. Maximum F-measure for Detecting Duplicate Field Values T-test results indicate differences are significant at .05 level 0.94 0.91 0.97 0.94 0.71 0.35 Learned Affine Edit Dist. 0.92 0.89 0.95 0.93 0.68 0.29 Static Affine Edit Dist. Citeseer Constraint Citeseer RL Citeseer Face Citeseer Reason Restaurant Address RestaurantName Metric

67. Mailing List Record Results

68. Restaurant Record Results