Knowledge discovery in high-dimensional data: case studies and a user survey for the rank-by-feature framework.
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.
Seo
Jinwook
J
Children's Research Institute, Washington, DC 20010, USA. jseo@cnmcresearch.org
Shneiderman
Ben
B
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003196:Computer Graphics; D003198:Computer Simulation; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008962:Models, Theoretical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D014584:User-Computer Interface