TY - JOUR T1 - Knowledge discovery in high-dimensional data: case studies and a user survey for the rank-by-feature framework JF - IEEE Transactions on Visualization and Computer Graphics Y1 - 2006 A1 - Seo,Jinwook A1 - Shneiderman, Ben KW - case study KW - Computer aided software engineering KW - Computer Society KW - Data analysis KW - data mining KW - data visualisation KW - Data visualization KW - database management systems KW - e-mail user survey KW - Genomics KW - Helium KW - Hierarchical Clustering Explorer KW - hierarchical clustering explorer. KW - high-dimensional data KW - Histograms KW - Information visualization evaluation KW - interactive systems KW - interactive tool KW - knowledge discovery KW - multivariate data KW - Rank-by-feature framework KW - Scattering KW - Testing KW - user interface KW - User interfaces KW - user survey KW - visual analytic tools KW - visual analytics KW - visualization tools AB - 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 three-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 rank-by-feature framework. Our own successes using HCE provided some testimonial evidence of its utility, but we felt it necessary to get beyond our subjective impressions. This paper presents an evaluation of the hierarchical clustering explorer (HCE) using three case studies and an e-mail user survey (n=57) to focus on skill acquisition with the novel concepts and interface for the rank-by-feature framework. Knowledgeable and motivated users in diverse fields provided multiple perspectives that refined our understanding of strengths and weaknesses. A user survey confirmed the benefits of HCE, but gave less guidance about improvements. Both evaluations suggested improved training methods VL - 12 SN - 1077-2626 CP - 3 M3 - 10.1109/TVCG.2006.50 ER -