1 / 14

Enhancing Interactive Visual Data Analysis by Statistical Functionality

Enhancing Interactive Visual Data Analysis by Statistical Functionality. Jürgen Platzer VRVis Research Center Vienna, Austria. Overview. Motivation Statistics Library for Information Visualization Sample Application Conclusions. Motivation.

bettyanderson
Download Presentation

Enhancing Interactive Visual Data Analysis by Statistical Functionality

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  2. Overview • Motivation • Statistics Library for Information Visualization • Sample Application • Conclusions Enhancing Interactive Visual Data Analysis by Statistical Functionality October 23, 2019

  3. Motivation • Information visualization and statistical methods try to enable a better insight into data • The same goal is reached by different means Information Visualization Statistical Routines • 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. • 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 Enhancing Interactive Visual Data Analysis by Statistical Functionality October 23, 2019

  4. 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. Enhancing Interactive Visual Data Analysis by Statistical Functionality October 23, 2019

  5. 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 Enhancing Interactive Visual Data Analysis by Statistical Functionality October 23, 2019

  6. 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 Enhancing Interactive Visual Data Analysis by Statistical Functionality October 23, 2019

  7. Statistics Library for InfoViz • Fuzzy k means (UVW dataset - 149 769 data items) Enhancing Interactive Visual Data Analysis by Statistical Functionality October 23, 2019

  8. 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 Enhancing Interactive Visual Data Analysis by Statistical Functionality October 23, 2019

  9. Sample Application • Interactive Clustering (Letter image recognition data – 4640 data items, 6 groups) Enhancing Interactive Visual Data Analysis by Statistical Functionality October 23, 2019

  10. Sample Application • Interactive Clustering (Letter image recognition data – 4640 data items, 6 groups) Enhancing Interactive Visual Data Analysis by Statistical Functionality October 23, 2019

  11. Sample Application • Interactive Clustering (Letter image recognition data – 4640 data items, 6 groups) Enhancing Interactive Visual Data Analysis by Statistical Functionality October 23, 2019

  12. 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 Enhancing Interactive Visual Data Analysis by Statistical Functionality October 23, 2019

  13. Acknowledgement • Peter Filzmoser • Helwig Hauser • Harald Piringer • Austrian research program Kplus Enhancing Interactive Visual Data Analysis by Statistical Functionality October 23, 2019

  14. Thank you for your attention.Are there any questions?

More Related