Chapter 13 References
·
Blake C., Keogh E., Merz CJ. UCI repository
of Machine Learning databases (machine readable data repository). Irvine, CA:
Department of Information and Computer Science, University of California at
Irvine. http://www.cs.uci.edu/mlearn/MLRepository.html (accessed 15 November
2002).
·
Blockeel H., Moyle S., Centralized model evaluation for
collaborative data mining. In M. Grobelnik, D. Mladenic, M. Bohanec, and M. Gams,
editors, Proceedings A of the 5th International Multi-Conference Information
Society, 2002: Data Mining and Data Warehousing/Intelligent Systems, pages
100–103. Jozef Stefan Institute, Ljubljana, Slovenia.
·
Chapman P., Clinton J., Kerber R., Khabaza T., Reinartz T.,
Shearer C., Wirth R., CRISPDM 1.0: step-by-step data mining guide, 2000.
·
Data Mining Group (DMG), PMML specification [WWW document]
http://www.dmg.org and http://sourceforge.net/projects/pmml/ (both accessed
09.05.2003).
·
Farrand J., ROCON, 2002, [WWW document] http://www.cs.bris.ac.uk/
farrand/rocon/ (accessed 22.05.2003).
·
Frank E., Hall M., Weka Boundary Visualizer, 2003, [WWW document]
http://www.cs.waikato.ac.nz/ml/weka/bvis/ (accessed 15.05.2003).
·
Gamberger D., Lavrac N., Wettschereck D., Subgroup Visualization:
A Method and Application in Population Screening. ECAI 2002 Workshop on
Intelligent Data Analysis in Medicine and Pharmacology, 2002
·
Jorge A., Poc¸as J, Azevedo P., Post-processing operators for
browsing large sets of association rules, in Proceedings of Discovery Science
02, Luebeck, Germany, LNCS 2534, Eds. Steffen Lange, Ken Satoh, Carl H. Smith,
Springer-Verlag, 2002
·
Mladenic D., Lavrac N., Bohanec M., Moyle S., editors, Data Mining
and Decision Support: Integration and Collaboration, Kluwer Academic Publishers,
2003
·
Provost F., Fawcett T., Robust Classification for Imprecise
Environments. Machine Learning 42(3): 203-231, 2001
·
Quinlan J., C4.5: Programs for Machine Learning. Machine Learning.
Morgan Kaufmann, San Mateo, CA, USA, 1993
·
Rheingans P., desJardins M., Visualizing high-dimensional
predictive model quality. In Proceedings of IEEE Visualization 2000, pages
493–496, 2000
·
Shneiderman B., Inventing discovery tools: combining information
visualization with data mining. Information Visualization 1:5-12. Palgrave
Macmillan Ltd, 2002
·
Stuttgart Neural Network Simulator [WWW document]
·
http://www-ra.informatik. unituebingen.de/SNNS/ (accessed
18.05.2003).
·
Thearling K, Becker B, DeCoste D, Mawby B, Pilote M, Sommerfield D
(2001) Information Visualization in Data Mining and Knowledge Discovery, Chapter
Visualizing Data Mining Models. Morgan Kaufmann.
·
Witten I., Frank E., Data Mining: Practical Machine Learning Tools
and Techniques with Java Implementations, Morgan Kaufmann, 1999
·
Wrobel S., An algorithm for multi-relational discovery of
subgroups. Proc. First European Symposium on Principles of Data Mining and
Knowledge Discovery, 78–87, Springer, 1997