1 Introduction Data Mining and Knowledge Discovery in Databases (KDD) are rapidly evolving areas of research that are at the intersection of several disciplines,
Patents & Publications
“Learning to Recognize Volcanoes on Venus” M.C. Burl, L. Asker, P. Smyth, U. Fayyad, P. Perona, L. Crumpler, and J. Aubele. Machine Learning, Vol.30, Issue 2, 165-194, February (1998).
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
View on http://dl.acm.org/
“Data mining and KDD: Promise and challenges” Fayyad, U. and Stolorz, P. Future Generation Computer Systems Vol.13, No.2, pp. 99-115, November (1997).
“Data Mining and Knowledge Discovery in Databases: Implications for Scientific Databases” Usama Fayyad. SSDBM 1998, Pro. of the 9th International Conference on Scientific and Statistical Database Management , pp. 2-11, IEEE Computer Society, Evergreen State College, Olympia, WA, August (1997).
On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
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“Application of Classification and Clustering to Sky Survey Cataloging and Analysis” U. Fayyad, S. Djorgovski, and N. Weir. Journal of Computational Statistics and Data Analysis, (1997)
Data Mining and Knowledge Discovery: An International Journal
View on http://link.springer.com/
Levelwise Search and Borders of Theories in Knowledge Discovery
View on www.dl.acm.org
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
View on http://www.learningace.com/