Mining Negative Association Rules

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# Mining Negative Association Rules - PowerPoint PPT Presentation

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

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
• 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
• Called LOS and denote as[i1,i2…..,im]
• [IBM Aptiva , Compaq]
• Extend [IBM Aptiva , Compaq, Notebook]
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
• If rule r’：X’→Y is not support ,it may exist negative rule.
• Salience measure (distance betewwn conf level)
• E()：estimated conf
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
• Equivalent or similar pair
• Exam：
• Negative rule and positive rule are couple