Direct Interesting Rule Generation
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Hiding informative association rule sets
Expert Systems with Applications: An International Journal
Finding association rules that trade support optimally against confidence
Intelligent Data Analysis
Expert Systems with Applications: An International Journal
Maintenance of informative ruler sets for predictions
Intelligent Data Analysis
Efficient sanitization of informative association rules
Expert Systems with Applications: An International Journal
Maintenance of sanitizing informative association rules
Expert Systems with Applications: An International Journal
An algorithm to mine general association rules from tabular data
Information Sciences: an International Journal
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Mining transaction databases for association rules usually generates a large number of rules, most of which are unnecessary when used for subsequent prediction. In this paper we define a rule set for a given transaction database that is much smaller than the association rule set but makes the same predictions as the association rule set by the confidence priority. We call this subset the informative rule set. The informative rule set is not constrained to particular target items; and it is smaller than the non-redundant association rule set. We present an algorithm to directly generate the informative rule set, i.e., without generating all frequentitemsets first, and that accesses the database less often than other unconstrained direct methods. We show experimentally that the informative rule set is much smaller than boththe association rule set and the non-redundant association rule set, and that it can be generated more efficiently.