标题:CONFIDENCE METRICS FOR ASSOCIATION RULE MINING.作者:Xiaowei Yan1
Chengqi Zhang1
Shichao Zhang2 zhangsc@it.uts.edu.au来源:Applied Artificial Intelligence; Sep2009, Vol. 23 Issue 8, p713-737, 25p, 1 graph文献类型:Article主题语:*VERSIFICATION
*TASK analysis
*DECISION making
*ALGORITHMS
ASSOCIATION rule mining摘要: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. [ABSTRACT FROM AUTHOR] Copyright of Applied Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)


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