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Mining Negative Association Rules

Mining Negative Association Rules. Xiaohui Yuan, Bill P. Buckles Zhaoshan Yuan Jian Zhang ISCC 2002. Outline. Motivation Problem define Algorithm Conclusion & Thought. Motivation. conf (age < 30 →coupe ) = 0.3/0.4 =75% conf (age > 30 → not buy coupe) = 0.5/0.6 = 83.3%. Problem.

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Mining Negative Association Rules

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  1. Mining Negative Association Rules Xiaohui Yuan, Bill P. Buckles Zhaoshan Yuan Jian Zhang ISCC 2002

  2. Outline • Motivation • Problem define • Algorithm • Conclusion & Thought

  3. Motivation • conf(age < 30 →coupe ) = 0.3/0.4 =75% • conf(age > 30 →not buy coupe) = 0.5/0.6 = 83.3%

  4. Problem • The difficulty for mining negative rules • Can’t simply pick threshold values for support and confidence. • Thousands of items are included in the transaction records.

  5. Positive association rule, supp() and conf()

  6. Negative association rule, supp() and conf()

  7. Sibling relationships • Called LOS and denote as[i1,i2…..,im] • [IBM Aptiva , Compaq] • Extend [IBM Aptiva , Compaq, Notebook]

  8. LOS • Locality of Similarity (LOS) • Similarity assumption • Sibling rule • If the item set X’ = {i1, i2,….ik,…,im} is the same as X except item ik is substituted for ih. • Rule r ：X→Y and Rule r’：X’→Y

  9. Discover Negitive Rule • If rule r’：X’→Y is not support ,it may exist negative rule. • Salience measure (distance betewwn conf level) • E()：estimated conf

  10. Condictions • for qualify a negative rule • there must exist a large deviation between the estimated and actual confidence. • the support and confidence are greater than the minima required.

  11. Pruning • Equivalent or similar pair • Exam： • Negative rule and positive rule are couple • Exam：”female →BuyHat “ and “﹁male→BuyHat”

  12. Algorithm

  13. Conclusion • It’s not depend on numbers of transactions • Complexity O(P x L) • P ：number of positive rule • L ：average size of LOS

  14. Thought • Estimate from postive to find negative cost , how ?

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