A SSOCIATION R ULES &amp; THE A PRIORI A LGORITHM

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# association rules the apriori algorithm - PowerPoint PPT Presentation

A SSOCIATION R ULES &amp; 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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### ASSOCIATION RULES & THE APRIORI ALGORITHM

BY: JOE CASABONA

INTRODUCTION
• Recap
• Data Mining
• Three types
• Association Rules
• Apriori Algorithm
ASSOCIATION RULES
• Most apparent form of Data Mining
• Objective: Find all co-occurrence relationships among data items
• Strength: Support & Confidence
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)
CONFIDENCE
• % of transactions where X also contains Y
• Determines predictability of the rule
• Min Support and Confidence Determined.
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
APRIORI ALGORITHM
• Generate all frequent item sets
• All item sets with min support
•  Generate all confident ARs from frequent item sets
• Downward Closure Property
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.
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
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
OTHER ALGORITHMS
• Eclat algorithm
• FP-Growth algorithm
• One-attribute-rule
• Zero-attribute-rule
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%
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