Data Mining: Opportunities and Challenges

The 20 contributions in this collection explore methods for extracting hidden predictive information from large databases, and present case studies drawn from the paper, health care, and banking industries. The approaches to data mining employ Bayesian analysis, feature selection, rough set theory, and self-organizing maps. Other topics include the control of inductive bias in supervised learning using evolutionary computation, cooperative learning and virtual reality- based visualization for data mining, and maximum performance efficiency approaches for estimating best practice costs. Annotation (c)2003 Book News, Inc., Portland, OR