Visualizing incomplete and partially ranked data
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Updated: Jun 28, 2026

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
Published on: January 2, 2011
Paul Kidwell1, Guy Lebanon, William S Cleveland
1Department of Statistics, Purdue University. kidwell@purdue.edu
Abstract:
Ranking data, which result from m raters ranking n items, are difficult to visualize due to their discrete algebraic structure, and the computational difficulties associated with them when n is large. This problem becomes worse when raters provide tied rankings or not all items are ranked. We develop an approach for the visualization of ranking data for large n which is intuitive, easy to use, and computationally efficient. The approach overcomes the structural and computational difficulties by utilizing a natural measure of dissimilarity for raters, and projecting the raters into a low dimensional vector space where they are viewed. The visualization techniques are demonstrated using voting data, jokes, and movie preferences.
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