Inproceedings,
Constraint-Based Rule Mining in Large, Dense Databases
page 188--197. (1999)
Abstract
: Constraint-based rule miners find all rules in a given data-set meeting user-specified constraints such as minimum support and confidence. We describe a new algorithm that exploits all userspecified constraints including minimum support, minimum confidence, and a new constraint that ensures every mined rule offers a predictive advantage over any of its simplifications. Our algorithm maintains efficiency even at low supports on data that is dense (e.g. relational data). Previous approaches such as Apriori and its variants exploit only the minimum support constraint, and as a result are ineffective on dense data due to a combinatorial explosion of "frequent itemsets". * Current affiliation: University of California at Riverside 1 1. Introduction Mining rules from data is a problem that has attracted considerable interest because a rule provides a concise statement of potentially useful information that is easily understood by end users. In the database literature, the focus has ...
