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Return to Integration and Mining
The subfield of itemset mining is essentially a collection of algorithms. Whenever a new type of constraint is discovered, a specialized algorithm is proposed to handle it. All of these algorithms are highly tuned to take advantage of the unique properties of their associated constraints, and so they are not very compatible with other constraints. In this paper we present a more unified view of mining constrained itemsets such that most existing algorithms can be easily extended to handle constraints for which they were not designed a-priori. We apply this technique to mining itemsets with restrictions on their variance - a problem that has been open for several years in the data mining community.
@inproceedings {DBLP:conf/pods/KiferGBW03, author = {Daniel Kifer and Johannes Gehrke and Cristian Bucila and Walker White}, booktitle = {PODS}, title = {How to quickly find a witness.}, pages = {272-283}, year = {2003}, url = {db/conf/pods/pods2003.html#KiferGBW03}, ee = {http://doi.acm.org/10.1145/773153.773180}, crossref = {conf/pods/2003}, bibsource = {DBLP, http://dblp.uni-trier.de} }  ©2004 Association for Computing Machinery |
