@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 {17259,
	title = {Interactively exploring hierarchical clustering results [gene identification]},
	journal = {Computer},
	volume = {35},
	year = {2002},
	month = {2002/07//},
	pages = {80 - 86},
	abstract = {To date, work in microarrays, sequenced genomes and bioinformatics has focused largely on algorithmic methods for processing and manipulating vast biological data sets. Future improvements will likely provide users with guidance in selecting the most appropriate algorithms and metrics for identifying meaningful clusters-interesting patterns in large data sets, such as groups of genes with similar profiles. Hierarchical clustering has been shown to be effective in microarray data analysis for identifying genes with similar profiles and thus possibly with similar functions. Users also need an efficient visualization tool, however, to facilitate pattern extraction from microarray data sets. The Hierarchical Clustering Explorer integrates four interactive features to provide information visualization techniques that allow users to control the processes and interact with the results. Thus, hybrid approaches that combine powerful algorithms with interactive visualization tools will join the strengths of fast processors with the detailed understanding of domain experts},
	keywords = {algorithmic methods, arrays, Bioinformatics, biological data sets, biology computing, Data analysis, data mining, data visualisation, Data visualization, DNA, Fluorescence, gene functions, gene identification, gene profiles, Genetics, Genomics, Hierarchical Clustering Explorer, hierarchical systems, interactive exploration, interactive information visualization tool, interactive systems, Large screen displays, meaningful cluster identification, metrics, microarray data analysis, pattern clustering, pattern extraction, Process control, Sensor arrays, sequenced genomes, Tiles},
	isbn = {0018-9162},
	doi = {10.1109/MC.2002.1016905},
	author = {Seo,Jinwook and Shneiderman, Ben}
}
