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Documents
Title:
Hierarchical Parallel Coordinates for Exploration of Large
Datasets
Authors: Ying-Huey Fua, Matthew O. Ward and Elke A. Rundensteiner
Abstract:
Our ability to accumulate large, complex (multivariate) data
sets has far exceeded our ability to effectively process them
in search of patterns, anomalies, and other interesting features.
Conventional multivariate visualization techniques generally do
not scale well with respect to the size of the data set. The focus
of this paper is on the interactive visualization of large multivariate
data sets based on a number of novel extensions to the parallel
coordinates display technique. We develop a multiresolutional
view of the data via hierarchical clustering, and use a variation
on parallel coordinates to convey aggregation information for
the resulting clusters. Users can then navigate the resulting
structure until the desired focus region and level of detail is
reached, using our suite of navigational and filtering tools.
We describe the design and implementation of our hierarchical
parallel coordinates system which is based on extending the XmdvTool
system. Lastly, we show examples of the tools and techniques applied
to large (hundreds of thousands of records) multivariate data
sets.
Keywords:
Large-scale multivariate data visualization, hierarchical
data exploration, parallel coordinates.
Source:
IEEE Conf. on Visualization '99, Oct. 1999.
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