1 / 46

Automated Conceptual Abstraction of Large Diagrams

Automated Conceptual Abstraction of Large Diagrams. By Daniel Levy and Christina Christodoulakis December 2012 (2 days before the end of the world). Outline. Introduction Big picture Clustering Algorithm Experiment & Results Conclusion. Outline. Introduction Big picture

parker
Download Presentation

Automated Conceptual Abstraction of Large Diagrams

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

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

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

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

  5. Outline Introduction Big picture Clustering Algorithm Experiment + Results Conclusion

  6. Big Picture

  7. Vision

  8. Diagram Abstraction

  9. Related Works Its been done before..

  10. Our Approach 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

  11. Our Approach

  12. Outline Introduction Big picture Clustering Algorithm Experiment + Results Conclusion

  13. Min-Cut

  14. Naïve Min-Cut Algorithm

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

  16. 4 4 E E A A 1 1 2 N N B B 3 C C

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

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

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

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

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

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

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

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

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

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

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

  28. Cluster Distance Measure We use RiTaWordNetgetDistance() 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

  29. Outline Introduction Big picture Clustering Algorithm Experiments + Results Conclusion

  30. Experiment 1

  31. Experiment #1

  32. User 1 abstraction Experiment # 1

  33. User 2 abstraction Experimentation

  34. automated abstraction Experiment # 1

  35. Experiment 2

  36. Experiment #2

  37. Simplified version

  38. Outline Introduction Big picture Clustering Algorithm Experiments + Results Conclusion

  39. Conclusions • 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

  40. Questions? Merry Christmas!

More Related