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dc.contributor.authorSeo, Jinwooken_US
dc.contributor.authorShneiderman, Benen_US
dc.date.accessioned2007-05-23T10:17:04Z
dc.date.available2007-05-23T10:17:04Z
dc.date.issued2005en_US
dc.identifier.urihttp://hdl.handle.net/1903/6516
dc.description.abstractExploratory analysis of multidimensional data sets is challenging because of the difficulty in comprehending more than three dimensions. Two fundamental statistical principles for the exploratory analysis are (1) to examine each dimension first and then find relationships among dimensions, and (2) to try graphical displays first and then find numerical summaries [1]. We implement these principles in a novel conceptual framework called the rank-by-feature framework. In the framework, users can choose a ranking criterion interesting to them and sort 1D or 2D axis-parallel projections according to the criterion. We introduce the rank-by-feature prism that is a color-coded lower-triangular matrix that guides users to desired features. Statistical graphs (histogram, boxplot, and scatterplot) and information visualization techniques (overview, coordination, and dynamic query) are combined to help users effectively traverse 1D and 2D axis-parallel projections, and finally to help them interactively find interesting features.en_US
dc.format.extent835961 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 2005-54en_US
dc.titleA Rank-by-Feature Framework for Unsupervised Multidimensional Data Exploration Using Low Dimensional Projections (2004)en_US
dc.typeTechnical Reporten_US
dc.contributor.departmentISRen_US


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