Visualizing differences in web search algorithms using the expected weighted hoeffding distance
Proceedings of the 19th international conference on World wide web
Quantitative data visualization with interactive KDE surfaces
Proceedings of the 26th Spring Conference on Computer Graphics
Clustering Algorithms for Chains
The Journal of Machine Learning Research
Visual comparison for information visualization
Information Visualization - Special issue on State of the Field and New Research Directions
Mode seeking over permutations for rapid geometric model fitting
Pattern Recognition
A generative model for rank data based on insertion sort algorithm
Computational Statistics & Data Analysis
Interactive bivariate mode trees for visual structure analysis
Proceedings of the 27th Spring Conference on Computer Graphics
A simultaneous sample-and-filter strategy for robust multi-structure model fitting
Computer Vision and Image Understanding
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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 rater, 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.