http://www.icmc.usp.br/~junio/PublishedPapers/RodriguesJr_et_al_Frequency_Plot-SIBGRAPI2003.pdf Jose Rodrigues, Agma J M Traina, Caetano Traina Jr (2003) Frequency Plot and Relevance Plot to Enhance Visual Data Exploration In: XVI Brazilian Symposium on Computer Graphics and Image Processing 117-124 IEEE Press. @inproceedings { DBLP:conf/sibgrapi/RodriguesTT03, title = "Frequency Plot and Relevance Plot to Enhance Visual Data Exploration", year = "2003", author = "Jose Rodrigues and Agma J M Traina and Caetano Traina Jr", booktitle = " XVI Brazilian Symposium on Computer Graphics and Image Processing", pages = "117-124", publisher = "IEEE Press", doi = "10.1109/SIBGRA.2003.1240999", url = "http://www.icmc.usp.br/~junio/PublishedPapers/RodriguesJr_et_al_Frequency_Plot-SIBGRAPI2003.pdf", urllink = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=1240999&", abstract = "We present two techniques aiming at exploring databases through multivariate visualizations. Both techniques intend to deal with the problem caused by the limited amount of elements that can be presented simultaneously in traditional visual exploration procedures. The first technique, the Frequency Plot, combines data frequency with interactive filtering to identify clusters and trends in subsets of the database. Thus, graphical elements (lines, pixels, icons, or graphical marks) are color differentiated proportionally to how frequent the value being represented is, while interactive filtering allows the selection of interesting partitions of the database. The second technique, the Relevance Plot, corresponds to assigning different levels of color distinguishably to visual elements according to their relevance to a user's specified data properties set, which can be chosen visually and dynamically.", keywords = "Computer science , Data analysis , Data visualization , Filtering , Frequency , Humans , Image databases , Information retrieval , Layout , Visual databases"}

1. Frequency Plot and Relevance
Plot to Enhance Visual Data
Exploration
http://www.icmc.usp.br/~junio/PublishedPapers/RodriguesJr_et_al_Frequency_Plot-SIBGRAPI2003.pdf
José Fernando Rodrigues Jr.
Agma J. M. Traina
Caetano Traina Jr.
Computer Science Department
University of Sao Paulo - Brazil

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Outline
•• MMoottiivvaattiioonn
• The GBDIView Tool
• Frequency Plot with Interactive Filtering
• Relevance Plot
• Visual Statistical Analysis
• Future Works and Conclusions

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Motivation
• Increasing volume of data that cannot be well
utilized to produce useful knowledge
• Raw Information Visualization techniques are
limited The in efficient the task use of data of the analysis
data can provide
helpful insight in critical decision
• Datasets might be making.
unlimited both in size and
complexity
• There is a need for visualization mechanisms that
reduce the drawback of massive datasets.

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The Problem
• Massively populated datasets tend to result in a
visualization scene with an unacceptable level of
cluttering;
• Some regions of the data seam like blots in the
visualization scene.
• Many Information Visualization
techniques have already been proposed to
attack these problems
• It is becoming each time more
challengeable to create new ones.

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Outline
• Motivation
• The GBDIView Tool
• Frequency Plot with Interactive Filtering
• Relevance Plot
• Visual Statistical Analysis
• Future Works and Conclusions

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The GBDIView Tool
A preliminary version of a
Visualization Environment, and a
partially working idea

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The GBDIView Tool
Features
• 4 well-known visualization techniques: Parallel
Coordinates, Scatter Plots, Star Coordinates, and
Table Lens
• Interaction with Link & Brush and interactive
filtering
• Basic statistics presentation
• Enabled with Frequency Plot and Relevance Plot

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Development
• Borland C++ Builder 5
• OpenGL
• Software Component
• Open source
Memory sharing and pipeline support.
Highly reusable code.

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Outline
• Motivation
• The GBDIView Tool
• Frequency Plot with Interactive Filtering
• Relevance Plot
• Visual Statistical Analysis
• Future Works and Conclusions

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Frequency Plot with
Interactive Filtering
• A method that combines the selective filtering
technique with an automatic statistical analysis
• The frequency here means how frequently a
given attribute value can be found in a dataset
• The frequency is visually presented through
the opacity of the graphical items

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Example

12. The Breast Cancer Dataset
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(Cortesy by the University of California
at Irvine Machine Learning Laboratory)
• 457 records
• 11 attributes: 1 sample identifier, 9 laboratorial
results, 1 attribute for classification
•Attribute “CLASS”: 0 for benign cancer and 1 for
malign

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Comparison
• The Frequency Plot is comparatively more
powerful than the raw visualization technique
• The probability analysis can reveal clusters in
subsets of the dataset
• The behavior of the data is immediately
characterized as the user interacts with it

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Outline
• Motivation
• The GBDIView Tool
• Frequency Plot with Interactive Filtering
• Relevance Plot
• Visual Statistical Analysis
• Future Works and Conclusions

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Relevance Plot
The data is presented accordingly to its relevance to a
user’s defined set of interesting points
X1
X1 = RP1 + MRD
Relevance = 0
X0
X0 = RP0
Relevance = 1
X2
X3
Null RP2  Not
Considered
Dist = 1
Relevance = - 1
The relevance point is over
the attribute value
The distance is equal the
Maximum relevance
distance The distance is the
maximum possible
Relevance = 1 + 0 + (-1) =
0/3 = 0

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Example

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Features of the Relevance Plot
• Provides an interactive fuzzy query in a
visual environment
• Allows to discover items of interest in a
speculative way
• Extends the interactive filtering approach

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Outline
• Motivation
• The GBDIView Tool
• Frequency Plot with Interactive Filtering
• Relevance Plot
•• VViissuuaall SSttaattiissttiiccaall AAnnaallyyssiiss
• Future Works and Conclusions

19. Visual Statistical Analysis
• Provides a summarization of the data being
visualized
• Visually demonstrates meaningful features of
the data
• Weaken the drawbacks of analysing too
populated data sets
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20. Visual Statistical Analysis
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Outline
• Motivation
• The GBDIView Tool
• Frequency Plot with Interactive Filtering
• Relevance Plot
• Visual Statistic Analysis
• Future Works and Conclusions

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Future Work: Possibilities
for Presentation
• Possibility of presentation through many visual
effects as size, color hue and color brightness
• Color mappings and 3D effects (depth perception)
might also be used

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Future Work: Possibilities
for Analysis
• Most basic schema: Euclidean distance, but
other distance schemas might be used for
additional insights
• Different distance calculus for different
dimensions
• Weights for the dimensions
• Customization

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Conclusions
• It is a challenge to discover new visualization techniques
that, in raw format, can contribute to visual analysis
• Visualization techniques should be improved by
automatic analysis mechanisms joined with interaction
techniques
• The Frequency Plot and Relevance Plot methods can
enhance visualization techniques of almost all kinds

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The End
Thanks for coming