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!