Objective-oriented utility-based association mining

The necessity of developing methods for discovering association patterns to increase business utility of an enterprise has long been recognized in the data mining community. This requires modeling specific association patterns that are both statistically (based on support and confidence) and semantically (based on objective utility) related to a given objective that a user wants to achieve or is interested in. However, no such general model has been reported in the literature. Traditional association mining focuses on deriving correlations among a set of items and their association rules; diaper /spl rarr/ beer only tells us that a pattern like {diaper} is statistically related to an item like beer. In this paper we present a new approach, called objective-oriented utility-based association (OOA) mining, to modeling such association patterns that are explicitly related to a user's objective and its utility. Due to its focus on a user's objective and the use of objective utility as key semantic information to measure the usefulness of association patterns, OOA mining differs significantly from existing approaches such as existing constraint-based association mining. We formally define OOA mining and develop an algorithm for mining OOA rules. The algorithm is an enhancement of a priori with specific mechanisms for handling objective utility. We prove that the utility constraint is neither monotone nor anti-monotone, succinct or convertible and present a novel pruning strategy based on the utility constraint to improve the efficiency of OOA mining.