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Query flocks: a generalization of association-rule mining

By D. Tsur, Jeffrey D. Ullman, Serge Abiteboul, Christopher Clifton, Rajeev Motwani, Svetlozar Nestorov and Arnon Rosenthal


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

Year: 1998
OAI identifier: oai:CiteSeerX.psu:
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