ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
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Isolated items discarding strategy for discovering high utility itemsets
Data & Knowledge Engineering
Mining long high utility itemsets in transaction databases
SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
Mining high utility itemsets in large high dimensional data
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Guest editorial: special issue on utility-based data mining
Data Mining and Knowledge Discovery
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Decision Support Systems
Mining long high utility itemsets in transaction databases
WSEAS Transactions on Information Science and Applications
Good classification tests as formal concepts
ICFCA'12 Proceedings of the 10th international conference on Formal Concept Analysis
Mining high utility quantitative association rules
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Confirmation measures of association rule interestingness
Knowledge-Based Systems
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The necessity to develop methods for discovering associationpatterns to increase business utility of an enterprisehas long been recognized in data mining community.This requires modeling specific association patterns thatare both statistically (based on support and confidence) andsemantically (based on objective utility) relating to a givenobjective that a user wants to achieve or is interested in.However, we notice that no such a general model has beenreported in the literature. Traditional association miningfocuses on deriving correlations among a set of items andtheir association rules like diaper 驴 beer only tell us thata pattern like fdiaperg is statistically related to an itemlike beer. In this paper, we present a new approach, calledObjective-Oriented utility-based Association (OOA)mining,to modeling such association patterns that are explicitlyrelating to a user's objective and its utility. Due to its focuson a user's objective and the use of objective utility as keysemantic information to measure the usefulness of associationpatterns, OOA mining differs significantly from existingapproaches such as the existing constraint-based associationmining. We formally define OOA mining and developan algorithm for mining OOA rules. The algorithm is anenhancement to Apriori with specific mechanisms for handlingobjective utility. We prove that the utility constraint isneither monotone nor anti-monotone nor succinct nor convertibleand present a novel pruning strategy based on theutility constraint to improve the efficiency of OOA mining.