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 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.
Wegman
Edward J
EJ
Center for Computational Statistics, George Mason University, Fairfax, VA 22030-4444, USA. ewegman@gmu.edu
D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D003627:Data Interpretation, Statistical