1 / 18

Uncovering Clusters in Crowded Parallel Coordinates Visualizations

Uncovering Clusters in Crowded Parallel Coordinates Visualizations. Alimir Olivettr Artero , Maria Cristina Ferreiara de Oliveira, Haim levkowitz Information Visualization 2004. Abstract. The idea is inspired by traditional image processing techniques such as grayscale manipulation.

lang
Télécharger la présentation

Uncovering Clusters in Crowded Parallel Coordinates Visualizations

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. Uncovering Clusters in Crowded Parallel Coordinates Visualizations AlimirOlivettrArtero, Maria Cristina Ferreiara de Oliveira, Haimlevkowitz Information Visualization 2004

  2. Abstract • The idea is inspired by traditional image processing techniques such as grayscale manipulation. • Reducing visual clutter and allowing the analyst to observe relevant patterns in the parallel coordinates.

  3. Introduction • The strong overlapping of graphical markers hampers the user’s ability to identify patterns in the data when the number of records and the dimensionality of the data set are high. • It is important to avoid displaying irrelevant information and enhancing the presentation of the useful one.

  4. Introduction • Tackling this problem with a strategy that computes frequency and density information, and uses them in parallel coordinates visualizations to filter out the information to be presented to the user.

  5. Frequency Information • The frequency function for a n-dimensional variable x is defined as : where h is the size of bins, σ is the number of records in the same bin, m is the number of all records.

  6. Frequency Information • A two-dimensional matrix is generated to store the frequency of each pair of attribute values, which is then used to draw the polygonal lines for the records in the data set. • For a data set with n attributes, n-1 frequency matrices are generated, one for each pair of attributes.

  7. Frequency Information • All the non-zero matrix elements generate a line segment in the visualization and the pixel intensity used to draw the line segment. • Each line segment is drawn with the Bresenham algorithm:

  8. Interactive Parallel Coordinates Frequency and Density plots • The intensity of the pixel with coordinates (q,p) is given by: • Square wave smoothing filter is used for each pixel:

  9. Interactive Parallel Coordinates Frequency and Density plots • S is a scaling factor.

  10. Density Information • The density function for a n-dimensional variable x is defined as : where diis the i-th record of the data set and K is the kernel function, the parameter defines a smoothing factor or bandwidth.

  11. visualizations of the Pollen data a) Frequency Plot b) Density Plot

  12. Interactive high-dimensional clustering with IPC plot

  13. Interactive high-dimensional clustering with IPC plot

  14. Interactive high-dimensional clustering with IPC plot

  15. Interactive high-dimensional clustering with IPC plot

  16. Interactive high-dimensional clustering with IPC plot

  17. Performance • Running times in seconds for the proposed algorithm with different values of m and n.

  18. Conclusions • The new plots support interactive data exploration of large and high-dimensional data sets, allowing users to remove noise and highlight areas with high concentration of data. • The proposed algorithms use only integer arithmetic to compute the frequency matrices.

More Related