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  1. Proceedings of the 1999 workshop on new paradigms in information visualization and manipulation in conjunction with the eighth ACM internation conference on Information and knowledge management (NPIVM '99)
  2. Visualization of high-dimensional model characteristics
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Information visualization using a new focus+context technique in combination with dynamic clustering of information space
Visualization of high-dimensional model characteristics
Dimensional anchors: a graphic primitive for multidimensional multivariate information visualizations
Using shape to visualize multivariate data
Human-guided simple search: combining information visualization and heuristic search
Temporal, geographical and categorical aggregations viewed through coordinated displays: a case study with highway incident data
Virtual environments for geographic visualization: potential and challenges
Audio-visual data mapping for GIS-based data: an experimental evaluation
Hypercept: behavioural linkage in hypertext environments
Navigation in huge information hierarchies application to network management
Conceptualizing bandwidth allocation in network management
Almost: exploring program traces
Interactive video cubism
Exploration of large image collections using virtual reality devices
Application redirection: hosting Windows applications in 3D
3DIVS: 3-dimensional immersive virtual sculpting

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Visualization of high-dimensional model characteristics

Content Provider ACM Digital Library
Author Rheingans, Penny desJardins, Marie
Abstract Using inductive learning techniques to construct explanatory models for large, high-dimensional data sets is a useful way to discover useful information. However, these models can be difficult for users to understand. We have developed a set of visualization methods that enable a user to evaluate the quality of learned models, to compare alternative models, and identify ways in which a model might be improved We describe the visualization techniques we have explored, including methods for high-dimensional data space projection, variable/class correlation, instance mapping, and model sampling We show the results of applying these techniques to several models built from a benchmark data set of census data.
Starting Page 6
Ending Page 8
Page Count 3
ISBN 1581132549
DOI 10.1145/331770.331774
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 1999-11-01
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Visualization Data mining and knowledge discovery Multidimensional information spaces
Content Type Text
Resource Type Article
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