Mining Interesting Imperfectly Sporadic Rules

  • Yun Sing Koh
  • Nathan Rountree
  • Richard O’Keefe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3918)


Detecting association rules with low support but high confidence is a difficult data mining problem. To find such rules using approaches like the Apriori algorithm, minimum support must be set very low, which results in a large amount of redundant rules. We are interested in sporadic rules; i.e. those that fall below a maximum support level but above the level of support expected from random coincidence. In this paper we introduce an algorithm called MIISR to find a particular type of sporadic rule efficiently: where the support of the antecedent as a whole falls below maximum support, but where items may have quite high support individually. Our proposed method uses item constraints and coincidence pruning to discover these rules in reasonable time.


Association Rule Frequent Itemsets Mining Association Rule Support Threshold Inverted Index 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yun Sing Koh
    • 1
  • Nathan Rountree
    • 1
  • Richard O’Keefe
    • 1
  1. 1.Department of Computer ScienceUniversity of OtagoNew Zealand

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