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Enhancing Interactive Visual Data Analysis by Statistical Functionality Jürgen Platzer VRVis Research Center Vienna, Austria.

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Presentation on theme: "Enhancing Interactive Visual Data Analysis by Statistical Functionality Jürgen Platzer VRVis Research Center Vienna, Austria."— Presentation transcript:

1 http://www.VRVis.at/ Enhancing Interactive Visual Data Analysis by Statistical Functionality Jürgen Platzer VRVis Research Center Vienna, Austria

2 Jürgen Platzer 2Enhancing Interactive Visual Data Analysis by Statistical Functionality October 20, 2015 Overview  Motivation  Statistics Library for Information Visualization  Sample Application  Conclusions

3 Jürgen Platzer 3Enhancing Interactive Visual Data Analysis by Statistical Functionality October 20, 2015 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

4 Jürgen Platzer 4Enhancing Interactive Visual Data Analysis by Statistical Functionality October 20, 2015 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.

5 Jürgen Platzer 5Enhancing Interactive Visual Data Analysis by Statistical Functionality October 20, 2015 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

6 Jürgen Platzer 6Enhancing Interactive Visual Data Analysis by Statistical Functionality October 20, 2015 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

7 Jürgen Platzer 7Enhancing Interactive Visual Data Analysis by Statistical Functionality October 20, 2015 Statistics Library for InfoViz  Fuzzy k means (UVW dataset - 149 769 data items)

8 Jürgen Platzer 8Enhancing Interactive Visual Data Analysis by Statistical Functionality October 20, 2015 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

9 Jürgen Platzer 9Enhancing Interactive Visual Data Analysis by Statistical Functionality October 20, 2015 Sample Application  Interactive Clustering (Letter image recognition data – 4640 data items, 6 groups)

10 Jürgen Platzer 10Enhancing Interactive Visual Data Analysis by Statistical Functionality October 20, 2015 Sample Application  Interactive Clustering (Letter image recognition data – 4640 data items, 6 groups)

11 Jürgen Platzer 11Enhancing Interactive Visual Data Analysis by Statistical Functionality October 20, 2015 Sample Application  Interactive Clustering (Letter image recognition data – 4640 data items, 6 groups)

12 Jürgen Platzer 12Enhancing Interactive Visual Data Analysis by Statistical Functionality October 20, 2015 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

13 Jürgen Platzer 13Enhancing Interactive Visual Data Analysis by Statistical Functionality October 20, 2015 Acknowledgement  Peter Filzmoser  Helwig Hauser  Harald Piringer  Austrian research program Kplus

14 http://www.VRVis.at/ Thank you for your attention. Are there any questions?

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