Objective-Oriented Utility-Based Association Mining

The necessity to develop methods for discovering association patterns to increase business utility of an enterprise has long been recognized in data mining community. This requires modeling specific association patterns that are both statistically (based on support and confidence) and semantically (based on objective utility) relating to a given objective that a user wants to achieve or is interested in. However, we notice that no such a general model has been reported in the literature. Traditional association mining focuses on deriving correlations among a set of items and their association rules like only tell us that a pattern like is statistically related to an item like . In this paper, we present a new approach, called Objective-Oriented utility-based Association (OOA) mining, to modeling such association patterns that are explicitly relating 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 the existing constraint-based association mining. We formally define OOA mining and develop an algorithm for mining OOA rules. The algorithm is an enhancement to Apriori with specific mechanisms for handling objective utility. We prove that the utility constraint is neither monotone nor anti-monotone nor succinct nor convertible and present a novel pruning strategy based on the utility constraint to improve the efficiency of OOA mining.