Model space visualization for multivariate linear trend discovery

Discovering and extracting linear trends and correlations in datasets is very important for analysts to understand multivariate phenomena. However, current widely used multivariate visualization techniques, such as parallel coordinates and scatterplot matrices, fail to reveal and illustrate such lin...

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Bibliographic Details
Published in:2009 IEEE Symposium on Visual Analytics Science and Technology pp. 75 - 82
Main Authors: Zhenyu Guo, Ward, M.O., Rundensteiner, E.A.
Format: Conference Proceeding
Language:English
Published: IEEE 01.10.2009
Subjects:
Computer science
Data analysis
Data mining
Data visualization
Extraterrestrial phenomena
Knowledge Discovery
model space visualization
multivariate linear model construction
Navigation
Pattern analysis
Predictive models
Scattering
User interfaces
visual analysis
ISBN:9781424452835, 142445283X
Online Access:Get full text
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Summary:Discovering and extracting linear trends and correlations in datasets is very important for analysts to understand multivariate phenomena. However, current widely used multivariate visualization techniques, such as parallel coordinates and scatterplot matrices, fail to reveal and illustrate such linear relationships intuitively, especially when more than 3 variables are involved or multiple trends coexist in the dataset. We present a novel multivariate model parameter space visualization system that helps analysts discover single and multiple linear patterns and extract subsets of data that fit a model well. Using this system, analysts are able to explore and navigate in model parameter space, interactively select and tune patterns, and refine the model for accuracy using computational techniques. We build connections between model space and data space visually, allowing analysts to employ their domain knowledge during exploration to better interpret the patterns they discover and their validity. Case studies with real datasets are used to investigate the effectiveness of the visualizations.
ISBN:9781424452835
142445283X
DOI:10.1109/VAST.2009.5333431