Data Mining and Knowledge Discovery

, Volume 30, Issue 4, pp 928–963 | Cite as

Mining significant association rules from uncertain data

  • Anshu Zhang
  • Wenzhong Shi
  • Geoffrey I. Webb


In association rule mining, the trade-off between avoiding harmful spurious rules and preserving authentic ones is an ever critical barrier to obtaining reliable and useful results. The statistically sound technique for evaluating statistical significance of association rules is superior in preventing spurious rules, yet can also cause severe loss of true rules in presence of data error. This study presents a new and improved method for statistical test on association rules with uncertain erroneous data. An original mathematical model was established to describe data error propagation through computational procedures of the statistical test. Based on the error model, a scheme combining analytic and simulative processes was designed to correct the statistical test for distortions caused by data error. Experiments on both synthetic and real-world data show that the method significantly recovers the loss in true rules (reduces type-2 error) due to data error occurring in original statistically sound method. Meanwhile, the new method maintains effective control over the familywise error rate, which is the distinctive advantage of the original statistically sound technique. Furthermore, the method is robust against inaccurate data error probability information and situations not fulfilling the commonly accepted assumption on independent error probabilities of different data items. The method is particularly effective for rules which were most practically meaningful yet sensitive to data error. The method proves promising in enhancing values of association rule mining results and helping users make correct decisions.


Pattern discovery Association rules Statistical evaluation Uncertain data 



We wish to thank the action editor Mohammed J. Zaki and the anonymous reviewers for their insightful comments which have helped very much on improving the manuscript. This study is partially supported by the Special Fund for Technological Leading Talents, National Administration of Surveying, Mapping and Geoinformation, P.R. China.


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Copyright information

© The Author(s) 2016

Authors and Affiliations

  1. 1.Department of Land Surveying and Geo-InformaticsThe Hong Kong Polytechnic UniversityHung Hom, KowloonPeople’s Republic of China
  2. 2.Faculty of Information TechnologyMonash UniversityMelbourneAustralia

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