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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

Mining Negative Association Rules

Xiaohui Yuan, Bill P. Buckles

Zhaoshan Yuan

Jian Zhang

ISCC 2002

outline
Outline
  • Motivation
  • Problem define
  • Algorithm
  • Conclusion & Thought
motivation
Motivation
  • conf(age < 30 →coupe ) = 0.3/0.4 =75%
  • conf(age > 30 →not buy coupe) = 0.5/0.6 = 83.3%
problem
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.
sibling relationships
Sibling relationships
  • Called LOS and denote as[i1,i2…..,im]
    • [IBM Aptiva , Compaq]
    • Extend [IBM Aptiva , Compaq, Notebook]
slide8
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
discover negitive rule
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
condictions
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.
pruning
Pruning
  • Equivalent or similar pair
    • Exam:
  • Negative rule and positive rule are couple
    • Exam:”female →BuyHat “ and “﹁male→BuyHat”
conclusion
Conclusion
  • It’s not depend on numbers of transactions
  • Complexity O(P x L)
    • P :number of positive rule
    • L :average size of LOS
thought
Thought
  • Estimate from postive to find negative cost , how ?