@conference {16944,
title = {A Rank-by-Feature Framework for Unsupervised Multidimensional Data Exploration Using Low Dimensional Projections},
booktitle = {IEEE Symposium on Information Visualization, 2004. INFOVIS 2004},
year = {2004},
month = {2004///},
pages = {65 - 72},
publisher = {IEEE},
organization = {IEEE},
abstract = {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 (D.S. Moore, (1999). 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},
keywords = {axis-parallel projections, boxplot, color-coded lower-triangular matrix, computational complexity, computational geometry, Computer displays, Computer science, Computer vision, Data analysis, data mining, data visualisation, Data visualization, Displays, dynamic query, Educational institutions, exploratory data analysis, feature detection, feature detection/selection, Feature extraction, feature selection, graph theory, graphical displays, histogram, Information Visualization, interactive systems, Laboratories, Multidimensional systems, Principal component analysis, rank-by-feature prism, scatterplot, statistical analysis, statistical graphics, statistical graphs, unsupervised multidimensional data exploration, very large databases},
isbn = {0-7803-8779-3},
doi = {10.1109/INFVIS.2004.3},
author = {Seo,J. and Shneiderman, Ben}
}