Abstract
Industrial and commercial firms accumulate vast quantities of data in the course of their day-to-day business. The primary use of this data is to monitor business processes: inventory, maintenance actions, and so on. However this data contains much valuable information that, if accessible, would enhance the understanding of, and aid in improving the performance of, the processes being monitored.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone. Classification and Regression Trees. Wadsworth & Brooks, 1984.
Wray Buntine and Rich Caruana. Introduction to IND and recursive partitioning. NASA Ames Research Center, Mail Stop 269–2 Moffet Field, CA 94035, September 1991.
Peter Clark and Tim Niblett. The CN2 induction algorithm. Machine Learning,3(4):261–284, March 1989.
D. Madigan and A. E. Raftery. Model selection and accounting for model uncertainty in graphical models using Occam’s window. Journal of the American Statistical Association, 89(4):1535–1546, 1994.
R.E. Kass and A. E. Raftery. Bayes factors and model uncertainty. Technical Report 254, Department of Statistics, University of Washington, Seattle, Wash. 98195, U.S.A., 1991.
P.J. Riddle, R. Segal, and O. Etzioni. Representation design and brute-force induction in a Boeing manufacturing domain. Applied Artificial Intelligence, 8(1):125–147, 1994.
J.W. Tukey. An alphabet for statisticians’ expert systems. In W. A. Gale, editor, Artificial Intelligence and Statistics. Addison-Wesley, Reading, Mass, 1986.
J. Whittaker. Graphical Models in Applied Multivariate Statistics. John Wiley & Sons, Chichester, England and New York, 1990.
Y.M.M. Bishop, S.E. Fienberg, and P.W. Holland. Discrete Multivariate Analysis. The MIT Press, 1975.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1996 Springer-Verlag New York, Inc.
About this chapter
Cite this chapter
Riddle, P., Fresnedo, R., Newman, D. (1996). Framework for a Generic Knowledge Discovery Toolkit. In: Fisher, D., Lenz, HJ. (eds) Learning from Data. Lecture Notes in Statistics, vol 112. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2404-4_33
Download citation
DOI: https://doi.org/10.1007/978-1-4612-2404-4_33
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-94736-5
Online ISBN: 978-1-4612-2404-4
eBook Packages: Springer Book Archive