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Proceedings Paper

Visual cues for data mining
Author(s): Bernice E. Rogowitz; David A. Rabenhorst; John A. Gerth; Edward B. Kalin
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Paper Abstract

This paper describes a set of visual techniques, based on principles of human perception and cognition, which can help users analyze and develop intuitions about tabular data. Collections of tabular data are widely available, including, for example, multivariate time series data, customer satisfaction data, stock market performance data, multivariate profiles of companies and individuals, and scientific measurements. In our approach, we show how visual cues can help users perform a number of data mining tasks, including identifying correlations and interaction effects, finding clusters and understanding the semantics of cluster membership, identifying anomalies and outliers, and discovering multivariate relationships among variables. These cues are derived from psychological studies on perceptual organization, visual search, perceptual scaling, and color perception. These visual techniques are presented as a complement to the statistical and algorithmic methods more commonly associated with these tasks, and provide an interactive interface for the human analyst.

Paper Details

Date Published: 22 April 1996
PDF: 26 pages
Proc. SPIE 2657, Human Vision and Electronic Imaging, (22 April 1996); doi: 10.1117/12.238725
Show Author Affiliations
Bernice E. Rogowitz, IBM Thomas J. Watson Research Ctr. (United States)
David A. Rabenhorst, IBM Thomas J. Watson Research Ctr. (United States)
John A. Gerth, IBM Thomas J. Watson Research Ctr. (United States)
Edward B. Kalin, IBM Thomas J. Watson Research Ctr. (United States)


Published in SPIE Proceedings Vol. 2657:
Human Vision and Electronic Imaging
Bernice E. Rogowitz; Jan P. Allebach, Editor(s)

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