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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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