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2-D and 3-D Layouts to Aid Human Cognition of Local Structure in Multivariate Data

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dc.contributor.author Sun, Ru
dc.creator Sun, Ru
dc.date 2008-08-01
dc.date.accessioned 2008-08-21T19:38:19Z
dc.date.available NO_RESTRICTION en
dc.date.available 2008-08-21T19:38:19Z
dc.date.issued 2008-08-21T19:38:19Z
dc.identifier.uri http://hdl.handle.net/1920/3235
dc.description.abstract This dissertation addresses the development of new 2-D and 3-D layout algorithms for statistical visualization purposes. These layouts serve tasks that include placing near neighbors close together, showing group or cluster membership, allocating space for glyphs and images used to characterize objects (cases), and approximating distances between objects. These tasks serve goals that include conveying structure, facilitating pattern discovery and hypothesis generation, and providing access to detailed information. The layouts are for human use, so they include considerations of human perception, cognition, and organizational regularity. This dissertation targets applications involving the study of cases, variables, clusters, and other multivariate objects. In these applications the notion of distances/dissimilarities between objects is important. However, accurate distances can not be maintained in low dimensional views. Researchers have developed a variety of layout methods to represent multivariate objects (including data summaries) in low dimensions. Common layout algorithms include multidimensional scaling, Kohonen self-organizing maps, Treemaps and spring models. This dissertation compares and contrasts the new layout algorithms with previous methods, develops new star glyphs, and demonstrates the new algorithms using multivariate data produced by AIRS (Atmospheric InfraRed Sounder) and other datasets. en
dc.language.iso en_US en
dc.subject Visualization en
dc.subject Cluster en
dc.subject Layout en
dc.subject Hexagon Grid en
dc.subject Glyph en
dc.title 2-D and 3-D Layouts to Aid Human Cognition of Local Structure in Multivariate Data en
dc.type Dissertation en
thesis.degree.name Doctor of Philosophy in Computational Sciences and Informatics en
thesis.degree.level Doctoral en
thesis.degree.discipline Computational Sciences and Informatics en
thesis.degree.grantor George Mason University en


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