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Apriori algorithm

Apriori is the algorithm used for calculating association rules.

The Apriori Approach - A Candidate Generation & Test
• Initially, scan transaction database once to get frequent 1-itemset
• Generate length (k+1) candidate itemsets from length k frequent itemsets
• Test the candidates against database
• Terminate when no frequent or candidate set can be generated

The Apriori Algorithm Implementation Details

 How to generate candidates?

• Step 1: self-joining Lk
• Step 2: pruning

 Example of Candidate-generation: L3={abc, abd, acd, ace, bcd}

• Self-joining: L3*L3={abcd, abce, acde}
• abcd from abc and abd
• acde from acd and ace
• Pruning: All nonempty subsets of a frequent itemset must also be frequent
• acde is removed from candidates because ade is not in L3 so C4 = {abcd}
• abce is removed from candidates because bce is not in L3 so C4 = {abcd}

The Apriori Algorithm Computing Confidence

• Once all support counts for frequent item lists are in hand so:
confidence(A->B) = P(B\A) = supportcount(A∪B)/supportcount(A)

The Apriori Algorithm Improvements

 • Major computational challenges (related to the main loops in pseudo code)
 • Multiple scans of transactions in data set
 • Huge number of candidates and workload of support counting for candidates

Ideas to improve:

• Reduce passes of database scans
• Shrink number of candidates

• Facilitate support counting of candidates

For more informations you can check Wikipedia here  and Oracle documents page here


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