1. Automated Conceptual Abstraction of Large Diagrams By Daniel Levy and Christina Christodoulakis December 2012 (2 days before the end of the world)

2.  Introduction  Big picture  Clustering Algorithm  Experiment & Results  Conclusion Outline

3.  Introduction  Big picture  Clustering Algorithm  Experiments & Results  Conclusion Outline

4.  So what is this “clustering” you speak of?  Why do we need to cluster?  Reduce cognitive load Introduction

8.  Introduction  Big picture  Clustering Algorithm  Experiment + Results  Conclusion Outline

9. Big Picture

10. Vision

11. Diagram Abstraction

12.  Its been done before.. Related Works

13.  Consider a diagram stripped of semantics, or pre processed using methodologies in previous work  Cluster graph  Evaluate clusters proposed based on closeness of meaning in the node names Our Approach

15.  Introduction  Big picture  Clustering Algorithm  Experiment + Results  Conclusion Outline

16. Min-Cut

17. Naïve Min-Cut Algorithm

18. C A N B 1 2 3 C A N B 2 3 E 4 E 4 *Must result in exactly 2 partitions Combinations / Creating partitions *Assume there exist additional nodes

19. C A N B 1 2 3 C A N B 1 E E 4 4

20. C D A B 2 1 3 C D A B 2 Minimum sets C D A B 2 1 3 C D A B 2 3

21. D A B 1 3 2 D A B 3 2 D A B 1 3 2 D A B 2 Cycles

22. E D C A B 1 2 3 4 5 Listing the min-cuts

23. E D C A B 1 2 3 4 5

24. E D C A B 1 2 3 4 5

25. E D C A B 1 2 3 4 5

26. E D C A B 1 2 3 4 5

27. E D C A B 1 2 3 4 5 E D C A B 1 2 3 Outside-in approach

28. E D C A B 1 2 3 4 5 E D C A B 1 2 3 5

29. E D C A B 1 2 3 4 5 E D C A B 1 2 3 4

30. E D C A B 1 2 3 4 5

33.  We use RiTa WordNet getDistance() function  We calculate pairwise distances between nodes.  Select for each node the smallest distance between it and another node  Sum all minimum distances  Average over all nodes in candidate cluster Cluster Distance Measure

34.  Introduction  Big picture  Clustering Algorithm  Experiments + Results  Conclusion Outline

35. Experiment 1

36. Experiment #1

37. User 1 abstraction

38. Experimentation User 2 abstraction

39. Experiment # 1 automated abstraction

40. Experiment 2

41. Experiment #2

42. Simplified version

44.  Introduction  Big picture  Clustering Algorithm  Experiments + Results  Conclusion Outline

45.  Surprised at how similar manual clustering and automated clustering were.  Suggested improvements:  Automatic distance threshold  Creating subgraphs  Strictness of clustering (min # of clusters  Advanced min-cut discovery Conclusions

46. Questions? Merry Christmas!