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Knowledge discovery in high-dimensional data: case studies and a user survey for the rank-by-feature framework
2006
IEEE Transactions on Visualization and Computer Graphics
Knowledge discovery in high dimensional data is a challenging enterprise, but new visual analytic tools appear to offer users remarkable powers if they are ready to learn new concepts and interfaces. Our 3-year effort to develop versions of the Hierarchical Clustering Explorer (HCE) began with building an interactive tool for exploring clustering results. It expanded, based on user needs, to include other potent analytic and visualization tools for multivariate data, especially the
doi:10.1109/tvcg.2006.50
pmid:16640245
fatcat:chg2hdfvfvfijjpzsbwcsyayre