A Rank-by-Feature Framework for Unsupervised Multidimensional Data Exploration Using Low Dimensional Projections
Oct. 10, 2004 to Oct. 12, 2004
Jinwook Seo , University of Maryland at College Park
Ben Shneiderman , University of Maryland at College Park
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/INFVIS.2004.3
Exploratory 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 . 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.
information visualization, exploratory data analysis, dynamic query, feature detection/selection, statistical graphics
Jinwook Seo, Ben Shneiderman, "A Rank-by-Feature Framework for Unsupervised Multidimensional Data Exploration Using Low Dimensional Projections", IEEE_INFOVIS, 2004, Information Visualization, IEEE Symposium on, Information Visualization, IEEE Symposium on 2004, pp. 65-72, doi:10.1109/INFVIS.2004.3