http://www.VRVis.at/

Enhancing Interactive Visual Data Analysis

by Statistical Functionality

Jürgen PlatzerVRVis Research Center

Vienna, Austria

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Overview

Motivation

Statistics Library for Information Visualization

Sample Application

Conclusions

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Motivation Information visualization and statistical methods try to enable a

better insight into data

The same goal is reached by different means

User’s pattern recognition system

Creates interactively modifiable graphics

Allows interactive efficient information drill-down

Low dimensional features are easily detected and analyzed.

Linked views allow interactive investigation of functional coherences.

Statistical RoutinesInformation Visualization

Today’s computational possibilities

Computation of facts, summaries, models, ...

A large variety of algorithms for specific tasks (clustering, dimension reduction,...)

Based on the knowledgeable theory of data exploration

Considers multivariate relationships

Results can be easily reproduced

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Aim of this work

Put user’s input and algorithmic capabilities on the same level.

Let them interactively communicate

Show that the interactive combination of the strength of both fields makes visual data mining more efficient.

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Statistics Library for InfoViz

Find the most important statistical functions for explorative data analysis. Clustering (Hierarchical approaches, partitional

heuristics) Dimension reduction (MDS, PCA, SOM) Transformation of Dimensions (Linear vs. non-

linear) Statistical Moments (classic vs. robust) Regression

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Statistics Library for InfoViz Additionally include innovative concepts

Robustness Reduce influence of outliers Detect outliers Integration of multivariate outlier identification

Fuzzyness Data comes from real world The real world is not based on bits!-) Integrate uncertainty in clustering by fuzzy k

means

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Statistics Library for InfoViz Fuzzy k means (UVW dataset - 149 769 data items)

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Statistics Library for InfoViz

Create hooks of interaction

Allow the interactive communication between

algorithm and the user.

Immediate updates of summaries based on

selections

Translation of user action into parameter settings

Starting algorithms based on previous results

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Sample Application Interactive Clustering (Letter image recognition

data – 4640 data items, 6 groups)

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Sample Application Interactive Clustering (Letter image recognition

data – 4640 data items, 6 groups)

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Sample Application Interactive Clustering (Letter image recognition

data – 4640 data items, 6 groups)

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Conclusions Keyword: INTERACTIVITY

Immediate validation of results Immediate adaptation of algorithms Immediate numerical feedback of user actions

Information exchange user / algorithm = incorporation of multivariate features

Research of possible communication concepts between user and statistical algorithms

Translation of user actions into parameter settings

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Acknowledgement Peter Filzmoser Helwig Hauser Harald Piringer Austrian research program Kplus

http://www.VRVis.at/

Thank you for your attention.Are there any questions?