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

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

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

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.

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

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

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

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

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)

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)

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)

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

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

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