@article {17272,
	title = {Knowledge discovery in high-dimensional data: case studies and a user survey for the rank-by-feature framework},
	journal = {IEEE Transactions on Visualization and Computer Graphics},
	volume = {12},
	year = {2006},
	month = {2006/06//May},
	pages = {311 - 322},
	abstract = {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},
	keywords = {case study, Computer aided software engineering, Computer Society, Data analysis, data mining, data visualisation, Data visualization, database management systems, e-mail user survey, Genomics, Helium, Hierarchical Clustering Explorer, hierarchical clustering explorer., high-dimensional data, Histograms, Information visualization evaluation, interactive systems, interactive tool, knowledge discovery, multivariate data, Rank-by-feature framework, Scattering, Testing, user interface, User interfaces, user survey, visual analytic tools, visual analytics, visualization tools},
	isbn = {1077-2626},
	doi = {10.1109/TVCG.2006.50},
	author = {Seo,Jinwook and Shneiderman, Ben}
}
@article {16949,
	title = {A telescope for high-dimensional data},
	journal = {Computing in Science \& Engineering},
	volume = {8},
	year = {2006},
	month = {2006/04//March},
	pages = {48 - 53},
	abstract = {Muscular dystrophy is a degenerative disease that destroys muscles and ultimately kills its victims. Researchers worldwide are racing to find a cure by trying to uncover the genetic processes that cause it. Given that a key process is muscle development, researchers at a consortium of 10 institutions are studying 1,000 men and women, ages 18 to 40 years, to see how their muscles enlarge with exercise. The 150 variables collected for each participant will make this data analysis task challenging for users of traditional statistical software tools. However, a new approach to visual data analysis is helping these researchers speed up their work. At the University of Maryland{\textquoteright}s Human-Computer Interaction Library, we developed an interactive approach to let researchers explore high-dimensional data in an orderly manner, focusing on specific features one at a time. The rank-by-feature framework lets them adjust controls to specify what they{\textquoteright}re looking for, and then, with only a glance, they can spot strong relationships among variables, find tight data clusters, or identify unexpected gaps. Sometimes surprising outliers invite further study as to whether they represent errors or an unusual outcome. Similar data analysis problems come up in meteorology, finance, chemistry, and other sciences in which complex relationships among many variables govern outcomes. The rank-by-feature framework could be helpful to many researchers, engineers, and managers because they can then steer their analyses toward the action},
	keywords = {Chemistry, Data analysis, data mining, data visualisation, degenerative disease, Degenerative diseases, diseases, Finance, genetic process, Genetics, high-dimensional data, interactive data analysis, medical computing, Meteorology, muscle, muscle development, Muscles, muscular dystrophy, Rank-by-feature framework, software, software libraries, software tools, Telescopes, visual data, visual data analysis},
	isbn = {1521-9615},
	doi = {10.1109/MCSE.2006.21},
	author = {Shneiderman, Ben}
}
