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“Exploring High-D Spaces with Multiform Matrices and Small Multiples”. MacEachren, A., Dai, X., Hardisty, F., Guo, D., and Lengerich, G. Proc. IEEE Symposium on Information Visualization (2003), 31–38. http://www.geovista.psu.edu/. Mudit Agrawal Nathaniel Ayewah. The Plan. Motivation

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Exploring high d spaces with multiform matrices and small multiples

“Exploring High-D Spaces with Multiform Matrices and Small Multiples”

MacEachren, A., Dai, X., Hardisty, F., Guo, D., and Lengerich, G.

Proc. IEEE Symposium on Information Visualization (2003), 31–38.

http://www.geovista.psu.edu/

Mudit Agrawal

Nathaniel Ayewah


The plan
The Plan Multiples”

  • Motivation

  • Contribution

  • Analysis Methods

  • GeoVISTA studio

  • Conclusions


Motivation
Motivation Multiples”

  • Discover Multivariate relationships

  • Examine data from multiple perspectives

DATA  INFORMATION


Contribution
Contribution Multiples”

  • Visual analysis of multivariate data

    • Combinations of scatterplots, bivariate maps and space-filling displays

    • Conditional Entropyto identify interesting variables from a data-set, and to order the variables to show more information

    • Dynamic query/filtering called Conditioning


Contribution1
Contribution Multiples”

  • Back-end: Design Box

    Building of applications using visual programming tools

  • Front-end: GUI Box

    Visualizing data using the developed designs

Source: GeoVista Studio


Analysis methods

Analysis Methods Multiples”


Sorting
Sorting Multiples”

Analysis Methods

  • Nested sorting – sort a table on selected attributes

  • To understand the relationships between sorted variables and the rest

  • Permutation Matrix :

    • cell values are replaced by graphical depiction of value.

    • Rows/cols can be sorted to search for related entities

    • e.g.


Sorting1
Sorting Multiples”

Analysis Methods

  • Augmented seriation:

    • Organizing a set of objects along a single dimension using multimodal multimedia

  • Correlation matrices

  • Reorderable Matrices:

    • Simple interactive visualization artifact for tabular data

Source: (Siirtola, 1999)


Space filling visualization
Space-filling visualization Multiples”

Analysis Methods

Mosaic plot

Sunburst methods

Source: (Schedl, 2006)

Source: (Young, 1999)

Pixel-oriented methods

Source: (Keim, 1996)


Multiform bivariate small multiple
Multiform Bivariate Small Multiple Multiples”

Analysis Methods

  • Small Multiples

    A set of juxtaposed data representations that together support understanding of multivariate information

Source: (MacEachren, 2003)


Multiform bivariate matrix
Multiform Bivariate Matrix Multiples”

Analysis Methods

Source: (MacEachren, 2003)


Geovista studio

GeoVista Studio Multiples”


Demonstration
Demonstration Multiples”

  • Basic Demo

    • Application construction

    • Scatterplot, Geomap

    • Dynamic linking, eccentric labeling etc.



High dimensionality
High Dimensionality Multiples”

  • Interactive Feature Selection

    • Guo, D., 2003. Coordinating Computational and Visualization Approaches for Interactive Feature Selection and Mulivariate Clustering. Information Visualization 2(4): 232-246.


High dimensionality1
High Dimensionality Multiples”

  • “Goodness of Clustering”

    • high coverage

    • high density

    • high dependence

  • E.g.

    • Correlation

    • Chi-squared

    • Conditional Entropy

HIGH

HIGH

LOW


Conditional entropy

1 Multiples”

2

3

4

1

2

Conditional Entropy

  • Discretize two dimensions into intervals

    • Nested Means

mean

mean

mean

Source: (Guo, 2003)


Conditional entropy1
Conditional Entropy Multiples”

Source: (Guo, 2003)


Ordering dimensions
Ordering Dimensions Multiples”

  • Related dimensions should be close together

Sort Method: Minimum Spanning Tree

Sort By: Conditional Entropy

A

5

B

9

15

16

21

C

D

4

Ordering: B A D C

unsorted


Demonstration1
Demonstration Multiples”

  • Advanced Demo

    • Interactive Feature Selection

    • PCP, SOM, Matrix

    • Conditioning


Conclusions
Conclusions Multiples”

  • Strengths

    • Dynamic Linking of different representations

    • Visualizing clusters of dimensions

    • Rich and extensible toolbox

  • Weaknesses

    • Usability

    • Arrangement of Windows


References
References Multiples”

  • Guo, D., (2003). Coordinating Computational and Visualization Approaches for Interactive Feature Selection and Mulivariate Clustering. Information Visualization 2(4): 232-246.

  • Keim, D (1996) Pixel-oriented Visualization Techniques for Exploring Very Large Databases, Journal of Computational and Graphical Statistics.

  • Schedl, M (2006), CoMIRVA: Collection of Music Information Retrieval and Visualization Applications. Website. http://www.cp.jku.at/people/schedl/Research/Development/CoMIRVA/webpage/CoMIRVA.html

  • Siirtola, H. (1999), Interaction with the Reorderable Matrix. In E. Banissi, F. Khosrowshahi, M. Sarfraz, E. Tatham, and A. Ursyn, editors, Information Visualization IV '99, pages 272-277. Proceedings International Conference on Information Visualization.

  • Young, F (1999), Frequency Distribution Graphs (Visualizations) for Category Variables, unpublished. http://forrest.psych.unc.edu/research/vista-frames/help/lecturenotes/lecture02/repvis4a.html.


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