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Association Rules and the Apriori Algorithm

This article provides an introduction to association rules and the Apriori algorithm, two key concepts in data mining. It explains the objective of association rules, how support and confidence are calculated, and provides an example. The Apriori algorithm is also explained, outlining the steps to generate frequent item sets and candidate sets. Other algorithms like Eclat and FP-Growth are briefly mentioned. The article concludes with a sample dataset and references.

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Association Rules and the Apriori Algorithm

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  1. ASSOCIATION RULES & THE APRIORI ALGORITHM BY: JOE CASABONA

  2. INTRODUCTION • Recap • Data Mining • Three types • Association Rules • Apriori Algorithm

  3. ASSOCIATION RULES • Most apparent form of Data Mining • Objective: Find all co-occurrence relationships among data items  • Strength: Support & Confidence 

  4. 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)

  5. CONFIDENCE • % of transactions where X also contains Y • Determines predictability of the rule • Min Support and Confidence Determined. 

  6. 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 

  7. APRIORI ALGORITHM • Generate all frequent item sets • All item sets with min support •  Generate all confident ARs from frequent item sets • Downward Closure Property

  8. 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.

  9. 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

  10. 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

  11. OTHER ALGORITHMS • Eclat algorithm • FP-Growth algorithm • One-attribute-rule • Zero-attribute-rule

  12. 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% 

  13. 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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