Visual data mining

Data mining strategies are usually applied to opportunistically collected data and frequently focus on the discovery of structure such as clusters, bumps, trends, periodicities, associations and correlations, quantization and granularity, and other structures for which a visual data analysis is very...

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Published in:Statistics in medicine 2003-05, Vol.22 (9), p.1383-1397
Main Author: Wegman, Edward J.
Format: Article
Language:English
Subjects:
Biological and medical sciences
Computational Biology - methods
Computer Graphics
Data Interpretation, Statistical
EDA
grand tour
knowledge discovery
Medical sciences
parallel co-ordinates
saturation brushing
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description Data mining strategies are usually applied to opportunistically collected data and frequently focus on the discovery of structure such as clusters, bumps, trends, periodicities, associations and correlations, quantization and granularity, and other structures for which a visual data analysis is very appropriate and quite likely to yield insight. However, data mining strategies are often applied to massive data sets where visualization may not be very successful because of the limits of both screen resolution, human visual system resolution as well as the limits of available computational resources. In this paper I suggest some strategies for overcoming such limitations and illustrate visual data mining with some examples of successful attacks on high‐dimensional and large data sets. Copyright © 2003 John Wiley & Sons, Ltd.
doi_str_mv 10.1002/sim.1502
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subjects Biological and medical sciences
Computational Biology - methods
Computer Graphics
Data Interpretation, Statistical
EDA
grand tour
knowledge discovery
Medical sciences
parallel co-ordinates
saturation brushing
title Visual data mining
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