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- W2091750225 abstract "We propose a simple, novel, and yet effective confidence metric for measuring the interestingness of association rules. Distinguishing from existing confidence measures, our metrics really indicate the positively companionate correlations between frequent itemsets. Furthermore, some desired properties are derived for examining the goodness of confidence measures in terms of probabilistic significance. We systematically analyze our metrics and traditional ones, and demonstrate that our new algorithm significantly captures the mainstream properties. Our approach will be useful to many association analysis tasks where one must provide actionable association rules and assist users to make quality decisions." @default.
- W2091750225 created "2016-06-24" @default.
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- W2091750225 date "2009-10-16" @default.
- W2091750225 modified "2023-09-27" @default.
- W2091750225 title "CONFIDENCE METRICS FOR ASSOCIATION RULE MINING" @default.
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- W2091750225 doi "https://doi.org/10.1080/08839510903208062" @default.
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