Efficient methods exist for discovering association rules from large collections of data.
The number of discovered rules can, however, be so large that the rules cannot be presented to the user. We show how the set of rules can be pruned by forming rule covers. A rule cover is a subset of the original set of rules such that for each row in the relation there is an applicable rule in the cover if and only if there is an applicable rule in the original set. Pruning and grouping of discovered association rules. In Workshop Notes of the ECML-95 Workshop on Statistics, Machine Learning, and Knowledge Discovery in Databases, pp.