Mining Optimized Gain Rules for Numeric Attributes

—Association rules are useful for determining correlations between attributes of a relation and have applications in the marketing, financial, and retail sectors. Furthermore, optimized association rules are an effective way to focus on the most interesting characteristics involving certain attributes. Optimized association rules are permitted to contain uninstantiated attributes and the problem is to determine instantiations such that either the support, confidence, or gain of the rule is maximized. In this paper, we generalize the optimized gain association rule problem by permitting rules to contain disjunctions over uninstantiated numeric attributes. Our generalized association rules enable us to extract more useful information about seasonal and local patterns involving the uninstantiated attribute. For rules containing a single numeric attribute, we present an algorithm with linear complexity for computing optimized gain rules. Furthermore, we propose a bucketing technique that...

• Type: Conference
• Year: 1999
• Where: KDD
• Authors: Sergey Brin, Rajeev Rastogi, Kyuseok Shim
• Keywords: Association Rules, Data Mining, KDD 1999, Numeric Attribute, Optimized Gain