Visual Cues for Data Mining

Copyright 1996 Society of Photo-Optical Instrumentation Engineers. This paper was (will be) published in SPIE Proceedings and is made available as an electronic reprint [preprint] with permission of SPIE. Single print or electronic copies for personal use only are allowed. Systematic or multiple reproduction, distribution to multiple locations through an electronic listserver or other electronic means, duplication of any material in this paper for a fee or for commericial purposes, or modification of the content of the pater are all prohibited. By choosing to view or print this document, you agree to all the provisions of the copyright law protecting it.

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, inculding 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 ouliers, and discovering multivariate relationships among variables. These cues are derived from psychological studies of 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.

By: Bernice E. Rogowitz, David A. Rabenhorst, John A. Gerth and Edward B. Kalin

Published in: SPIE Proceedings, volume 2657, (no ), pages 275-301 in 1996

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