a ssociation r ules the a priori a lgorithm
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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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i ntroduction
INTRODUCTION
  • Recap
    • Data Mining
    • Three types
  • Association Rules
  • Apriori Algorithm
a ssociation r ules
ASSOCIATION RULES
  • Most apparent form of Data Mining
  • Objective: Find all co-occurrence relationships among data items 
  • Strength: Support & Confidence 
s upport
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
CONFIDENCE
  • % of transactions where X also contains Y
  • Determines predictability of the rule
  • Min Support and Confidence Determined. 
e xample
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
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
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
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
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
OTHER ALGORITHMS
  • Eclat algorithm
  • FP-Growth algorithm
  • One-attribute-rule
  • Zero-attribute-rule
s ample d ata
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
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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