Query Flocks: A Generalization of Association-Rule Mining
Dick Tsur (Hitachi)
Jeffrey Ullman (Stanford University)
Serge Abiteboul (INRIA)
Chris Clifton (MITRE)
Rajeev Motwani (Stanford University)
Svetlozar Nestorov (Stanford University)
Arnon Rosenthal (MITRE)

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.
The full version of this paper appears here.