Title: Hierarchical Exploration of Large Multivariate Data Sets

Authors: Jing Yang, Matthew O. Ward and Elke A. Rundensteiner

Abstract: Multivariate data visualization techniques are often limited in terms of the number of data records that can be simultaneously displayed in a manner that allows ready interpretation. Due to the size of the screen and number of pixels available, visualizing more than a few thousand data points generally leads to clutter and occlusion. This in turn restricts our ability to detect, classify, and measure phenomena of interest, such as clusters, anomalies, trends, and patterns. In this paper we describe our experiences in the development of multi-resolution visualization techniques for large multivariate data sets. By hierarchically clustering the data and displaying aggregation information for each cluster, we can examine the data set at multiple levels of abstraction. In addition, by providing powerful navigation and ltering operations, we can create an environment suitable for interactive exploration without overloading the user with dense information displays. In this paper, we illustrate that our hierarchical displays are general by successfully applying them to four popular yet non-scalable visualizations, namely parallel coordinates, glyphs, scatterplot matrices and dimensional stacking.

Source: Proc. Dagstuhl Seminar on Scientific Visualization 2000.

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