Query Flocks: A Generalization of Association-Rule Mining
Dick Tsur, Jeffrey D. Ullman, Serge Abiteboul, Chris Clifton, Rajeev Motwani, Svetlozar Nestorov, Arnon Rosenthal
Full Paper (PDF)

Abstract
Association-rule mining has proved a highly successful technique for extracting useful information from very large databases. This success is attributed not only to the appropriateness of the objectives, but to the fact that a number of new query-optimization ideas, such as the "a-priori" trick, make association-rule mining run much faster than might be expected. In this paper we see that the same tricks can be extended to a much more general context, allowing efficient mining of very large databases for many different kinds of patterns. The general idea, called "query flocks," is a generate-and-test model for data-mining problems. We show how the idea can be used either in a general-purpose mining system or in a next generation of conventional query optimizers.

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BIBTEX

@inproceedings{DBLP:conf/sigmod/TsurUACMNR98,
author = {Dick Tsur and
Jeffrey D. Ullman and
Serge Abiteboul and
Chris Clifton and
Rajeev Motwani and
Svetlozar Nestorov and
Arnon Rosenthal},
editor = {Laura M. Haas and
Ashutosh Tiwary},
title = {Query Flocks: A Generalization of Association-Rule Mining},
booktitle = {SIGMOD 1998, Proceedings ACM SIGMOD International Conference
on Management of Data, June 2-4, 1998, Seattle, Washington, USA},
publisher = {ACM Press},
year = {1998},
isbn = {0-89791-955-5},
pages = {1-12},
crossref = {DBLP:conf/sigmod/98},
bibsource = {DBLP, http://dblp.uni-trier.de}
}


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