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Outlier-Preserving Focus+Context Visualization in
Parallel Coordinates
Matej NovotnýComenius University
Bratislava, Slovakia
Helwig HauserVRVis Research Center
Vienna, Austria
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Our goal A parallel coordinates visualization that:
Employs Focus+Context
Handles outliers
Renders effectively
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Overview Motivation
Abstraction, Focus+Context Outliers
Workflow Binning Context
Benefits Bonus! Results and conclusions
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Parallel Coordinates Insight into multidimensional data Correlations, Groups, Outliers
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Parallel Coordinates Insight into multidimensional data Correlations, Groups, Outliers
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Parallel Coordinates Insight into multidimensional data Correlations, Groups, Outliers
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Large data visualization Large data cause clutter in visualization
16.000 records
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Large data visualization Transparency used to decrease clutter
16.000 records
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Large data visualization Transparency used to decrease clutter ?
32.000 records
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Large data visualization Transparency used to decrease clutter ??
64.000 records
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Large data visualization Transparency used to decrease clutter ???
100.000 records
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Large data visualization Transparency used to decrease clutter ???
Do these records belong to the main trend?
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Data abstraction Density-based representation of data
Trends are clearly visible
16 bins
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Related work Hierarchical Parallel Coordinates
(Fua et al., 1999)
Visual representationof clusters
Smooth transparency
Cluster centersemphasized
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Related work Revealing Structure within Clustered Parallel
Coordinates Displays (Johansson et al., 2005)
Textures, density
Transfer functions
Clusters
Outliers
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Outliers Different, sparse, rare
Why should we care? Investigation (special cases in simulations…) Security (network intrusion, suspicious activity…) Detect errors in data acquisition
Outliers can be considered in: Data space Screen space
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Outliers
Outliers are like kids.
If you leave them unattendedthey either get lostor they break stuff.
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Outliers Avoid losing them in visualization
e.g. due to transparency or abstraction
Improve data abstraction or F+C e.g. remove outliers from clustering
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Workflow
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Workflow
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Step 1: Binning 2D binning
Density-based rep. Screen-oriented Low memory demands
compared to nD
Every segmentseparately
Result = bin map
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Benefits of binning? Operations no longer depend
on the size of the input Information is preserved Variable precision of binning
Variable precision of visual output
Fine binning does not destroy details
Larger bins can be producedfrom finer bins
128x128 bins
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Step 2: Outlier detection Various criteria can be employed
e.g. isolated bins, median filter …
64x64 bin map 32x32 bin mapmedian filter
32x32 bin mapisolated bins
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Step 3: Generating Context Outliers → opaque lines Binned trends → quads
Population mapped to color intensity
No blending Low visual complexity
Rendering order according to population
8 bins
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Step 4: Add Focus
8 bins
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Benefits Operations performed on bin maps
Reduced complexity Results coherent with visual output More operations feasible – e.g. Clustering
Outliers handled separately Increased information value Clearer context
Output-sensitive implementation View divided into layers and segments
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Results Large data can be rendered and explored
3 millions records, 16 dimensions, 32 bins Binned in 30 sec, rendered instantly (3Ghz,64bit)
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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BONUS!
Clustering
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Clustering, step 0 Apply Gaussian to smooth out the bin map
Segmentation data, Green vs Darkness
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Clustering, further steps Start with the highest population Decrease the population threshold
Old clusters grow New clusters emerge
50% 20% 10% 0%
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Clustering results
R B G D S H
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Clustering results
R B G D S H
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Clustering results
R B G D S H
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Clustering results
R B G D S H
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Conclusions Data abstraction based on density rep.
Data operations - outlier detection, clustering
Focus+Context Variable context precision Outliers preserved
Much clearer view for large data Screen-oriented and output-sensitive Interactive visualization of large data
Outlier-Preserving Focus+Context Visualization in Parallel CoordinatesMatej Novotnýhttp://www.VRVis.at/
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Acknowledgements K-Plus Vega grant 1/3083/06. AVL List GmbH - data Juergen Platzer Prof. Peter Filzmoser Harald Piringer Michael Wohlfahrt
Thank you for your attention!