Knowledge Discovery in High Dimensional Data: Case Studies and a User Survey for an Information Visualization Tool

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dc.contributor.authorSeo, Jinwooken_US
dc.contributor.authorShneiderman, Benen_US
dc.contributor.departmentISRen_US
dc.date.accessioned2007-05-23T10:18:07Z
dc.date.available2007-05-23T10:18:07Z
dc.date.issued2005en_US
dc.description.abstractKnowledge 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 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 email 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.en_US
dc.format.extent310479 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/6566
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 2005-100en_US
dc.subjectinformation visualizationen_US
dc.titleKnowledge Discovery in High Dimensional Data: Case Studies and a User Survey for an Information Visualization Toolen_US
dc.typeTechnical Reporten_US

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