Usability Views Article Details  home | timeline | recent | popular | e-reports | userati | books | about  Knowledge Discovery in High Dimensional Data: Case Studies and a User Survey for an Information Visualization Tool (09 Sep 2005) 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 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 strenghs and weaknesses. A user survey confirmed the benefits of HCE, but gave less guidance about improvements. Both evaluations suggested improved training methods. Article URL: ftp://ftp.cs.umd.edu/pub/hcil/Reports-Abstracts-Bibliography/2005-22html/Seo-Shn-HCE-evaluation.htm Read 63 more articles from University Of Maryland sorted by date, popularity, or title. Next Article: Book Excerpt: Rich Ling's "The Mobile Connection"     This site is a labour of love built by Chris McEvoy