A ssociation r ules the a priori a lgorithm
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A SSOCIATION R ULES & THE A PRIORI A LGORITHM. BY : J OE C ASABONA. I NTRODUCTION. Recap Data Mining Three types Association Rules Apriori Algorithm. A SSOCIATION R ULES. Most apparent form of Data Mining Objective: Find all co-occurrence relationships among data items 

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A ssociation r ules the a priori a lgorithm l.jpg

ASSOCIATION RULES & THE APRIORI ALGORITHM

BY: JOE CASABONA


I ntroduction l.jpg
INTRODUCTION

  • Recap

    • Data Mining

    • Three types

  • Association Rules

  • Apriori Algorithm


A ssociation r ules l.jpg
ASSOCIATION RULES

  • Most apparent form of Data Mining

  • Objective: Find all co-occurrence relationships among data items 

  • Strength: Support & Confidence 


S upport l.jpg
SUPPORT

  • Those who buy X buy Y, where X and Y are sets

    • X => Y

  •  .count = number of occurences

  • n = number of total transactions

  •  Number produced is % of all transactions (T)


C onfidence l.jpg
CONFIDENCE

  • % of transactions where X also contains Y

  • Determines predictability of the rule

  • Min Support and Confidence Determined. 


E xample l.jpg
EXAMPLE

  • AR 1: Xbox ---> Controller

    • Support: 5/8

    • Confidence: 3/5

  •  AR 2: COD4 ---> Xbox

    • Support: 5/8

    • Confidence: 2/5

  • AR 1 passes, AR 2 fails 


A priori a lgorithm l.jpg
APRIORI ALGORITHM

  • Generate all frequent item sets

    • All item sets with min support

  •  Generate all confident ARs from frequent item sets

  • Downward Closure Property


G enerate f requent i tem s ets l.jpg
GENERATE FREQUENT ITEM SETS

  • Count supports of each individual item

  • Create a set F with all individual items with min support

  • Creates "Candidate Set" C[k] based on F[k-1].

  • Check each element c in C[k] to see if it meets min support

  • Return set of all frequent item sets.


G enerate c andidate s ets l.jpg
GENERATE CANDIDATE SETS

  • Create two sets differing only in the last element, based on some seed set

  • Join those item sets into c

  • Compare each subset s of c to F[k-1]- if s is not in F[k-1], delete it.

  • Return final candidate set


R ule g enerate l.jpg
RULE GENERATE

  • Take Frequent Item Set F

    • If {F[1], F[2],...F[k-1]} => {F[k]}meets some min confidence, make it a rule

    • Remove last element from antecedent, insert into consequent, check again


O ther a lgorithms l.jpg
OTHER ALGORITHMS

  • Eclat algorithm

  • FP-Growth algorithm

  • One-attribute-rule

  • Zero-attribute-rule


S ample d ata l.jpg
SAMPLE DATA

  • Xbox, Controller, COD4

  • Xbox, COD4

  • Xbox, Controller

  • Controller, COD4

  • Xbox, Rock Band, Controller

  • Xbox, PS3

  • COD4, COD5, Rock Band

  • COD4, Rock Band 

  • Min Support: 60%

  • Min Confidence: 50% 


R ererences l.jpg
RERERENCES

The Book I am using:

 Liu, Bing. Web Data Mining, Chapter 2: Association Rules and Sequential Patterns. Springer, December, 2006 

Wikipedia:

"Apriori Algorithm." http://en.wikipedia.org/wiki/Apriori_algorithm March 23, 2009

"Association rule learning." http://en.wikipedia.org/wiki/Association_rulesMarch 25, 2009


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