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SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
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Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
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IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
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VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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Industrial Conference on Data Mining: Advances in Data Mining, Applications in E-Commerce, Medicine, and Knowledge Management
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Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
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Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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ACM Computing Surveys (CSUR)
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Statistics and Computing
Interestingness measures for association rules based on statistical validity
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CIMMACS '10 Proceedings of the 9th WSEAS international conference on computational intelligence, man-machine systems and cybernetics
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This article utilizes stochastic ideas for reasoning about association rule mining, and provides a formal statistical view of this discipline. A simple stochastic model is proposed, based on which support and confidence are reasonable estimates for certain probabilities of the model. Statistical properties of the corresponding estimators, like moments and confidence intervals, are derived, and items and itemsets are observed for correlations. After a brief review of measures of interest of association rules, with the main focus on interestingness measures motivated by statistical principles, two new measures are described. These measures, called 驴- and 驴-precision, respectively, rely on statistical properties of the estimators discussed before. Experimental results demonstrate the effectivity of both measures.