Automated Conceptual Abstraction of Large Diagrams
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Automated Conceptual Abstraction of Large Diagrams By Daniel Levy and Christina Christodoulakis December 2012 (2 days before the end of the world)
description
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 - PowerPoint PPT Presentation
Transcript of Automated Conceptual Abstraction of Large Diagrams
Automated Conceptual Abstraction of Large Diagrams
By Daniel Levy and Christina ChristodoulakisDecember 2012
(2 days before the end of the world)
Introduction Big picture Clustering Algorithm Experiment & Results Conclusion
Outline
Introduction Big picture Clustering Algorithm Experiments & Results Conclusion
Outline
So what is this “clustering” you speak of? Why do we need to cluster? Reduce cognitive load
Introduction
IntroductionBig picture Clustering Algorithm Experiment + Results Conclusion
Outline
Big Picture
Vision
Diagram Abstraction
Its been done before..
Related Works
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
Our Approach
Introduction Big pictureClustering Algorithm Experiment + Results Conclusion
Outline
Min-Cut
Naïve Min-Cut Algorithm
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N B2
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E4
E4
*Must result in exactly 2 partitions
Combinations / Creating partitions
*Assume there exist additional nodes
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Minimum sets
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3 C
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2D
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Cycles
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Listing the min-cuts
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Listing the min-cuts
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Listing the min-cuts
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Listing the min-cuts
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Listing the min-cuts
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Outside-in approach
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Outside-in approach
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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
Introduction Big picture Clustering AlgorithmExperiments + Results Conclusion
Outline
Experiment 1
Experiment #1
Experiment # 1User 1 abstraction
ExperimentationUser 2 abstraction
Experiment # 1automated abstraction
Experiment 2
Experiment #2
Simplified version
Introduction Big picture Clustering Algorithm Experiments + ResultsConclusion
Outline
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
Questions?Merry Christmas!
