Mining Negative Association Rules
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Transcript of Mining Negative Association Rules
2006/9/7 1
Mining Negative Association Rules
Xiaohui Yuan, Bill P. Buckles
Zhaoshan Yuan
Jian Zhang
ISCC 2002
2006/9/7 3
Motivation
conf(age < 30 →coupe ) = 0.3/0.4 =75% conf(age > 30 →not buy coupe) = 0.5/0.6 =
83.3%
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Problem
The difficulty for mining negative rulesCan’t simply pick threshold values for support
and confidence.Thousands of items are included in the
transaction records.
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Positive association rule, supp() and conf()
( )supp X Y
( )( )
( )
P X Yconf X Y
P X
, , , and = 0X Y X I Y I X Y
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Negative association rule, supp() and conf()
( ) 1 ( )conf X Y conf X Y
( ) ( ) ( )supp X Y supp Y supp Y X
( ) ( ) ( )supp X Y supp X supp X Y
( )( ) (1 ( ))
1 ( )
P Yconf X Y conf Y X
P X
( ), , , and 0X Y X Y X I Y I X Y
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Sibling relationships
Called LOS and denote as[i1,i2…..,im][IBM Aptiva , Compaq]Extend [IBM Aptiva , Compaq, Notebook]
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LOS
Locality of Similarity (LOS)Similarity assumption
Sibling rule If the item set X’ = {i1, i2,….ik,…,im} is the same as X ex
cept item ik is substituted for ih.
Rule r : X→Y and Rule r’: X’→Y
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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
( ') E( ( '))SM conf r conf r
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Condictions
for qualify a negative rule
1. there must exist a large deviation between the estimated and actual confidence.
2. the support and confidence are greater than the minima required.
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Pruning
Equivalent or similar pair
Exam:
Negative rule and positive rule are couple
Exam:” female →BuyHat “ and “﹁male→BuyHat”
, andX Y Y X
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Conclusion
It’s not depend on numbers of transactions Complexity O(P x L)
P : number of positive ruleL : average size of LOS