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Apriori Algorithm Review for Finals.

SE 157B, Spring Semester 2007Professor Lee

ByGaurang Negandhi

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Overview

Definition of Apriori Algorithm Steps to perform Apriori Algorithm Apriori Algorithm Examples Pseudo Code for Apriori Algorithm Apriori Advantages/Disadvantages References

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Definition of Apriori Algorithm In computer science and data mining, Apriori is a classic algorithm for learning association rules.

Apriori is designed to operate on databases containing transactions (for example, collections of items bought by customers, or details of a website frequentation).

The algorithm attempts to find subsets which are common to at least a minimum number C (the cutoff, or confidence threshold) of the itemsets.

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Definition (contd.) Apriori uses a "bottom up" approach,

where frequent subsets are extended one item at a time (a step known as candidate generation, and groups of candidates are tested against the data.

The algorithm terminates when no further successful extensions are found.

Apriori uses breadth-first search and a hash tree structure to count candidate item sets efficiently.

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Steps to Perform Apriori Algorithm

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Apriori Algorithm Examples Problem Decomposition

Transaction ID Items Bought1 Shoes, Shirt, Jacket2 Shoes,Jacket3 Shoes, Jeans4 Shirt, Sweatshirt

If the minimum support is 50%, then {Shoes, Jacket} is the only 2- itemset that satisfies the minimum support.

Frequent Itemset Support{Shoes} 75%{Shirt} 50%{Jacket} 50%{Shoes, Jacket} 50%

If the minimum confidence is 50%, then the only two rules generated from this 2-itemset, that have confidence greater than 50%, are:

Shoes Jacket Support=50%, Confidence=66%Jacket Shoes Support=50%, Confidence=100%

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The Apriori Algorithm — Example

Scan D

itemset sup.{1} 2{2} 3{3} 3{4} 1{5} 3

C1

itemset sup.{1} 2{2} 3{3} 3{5} 3

L1

itemset sup{1 3} 2{2 3} 2{2 5} 3{3 5} 2

L2

itemset sup{1 2} 1{1 3} 2{1 5} 1{2 3} 2{2 5} 3{3 5} 2

C2 itemset{1 2}{1 3}{1 5}{2 3}{2 5}{3 5}

C2

Scan D

C3 itemset{2 3 5}

Scan D L3 itemset sup{2 3 5} 2

TID Items100 1 3 4200 2 3 5300 1 2 3 5400 2 5

Database DMin support =50%

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Pseudo Code for Apriori Algorithm

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Apriori Advantages/Disadvantages Advantages

Uses large itemset property Easily parallelized Easy to implement

Disadvantages Assumes transaction database is

memory resident. Requires many database scans.

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Summary Association Rules form an very applied data

mining approach. Association Rules are derived from frequent

itemsets. The Apriori algorithm is an efficient algorithm

for finding all frequent itemsets. The Apriori algorithm implements level-wise

search using frequent item property. The Apriori algorithm can be additionally

optimized. There are many measures for association

rules.

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References References Agrawal R, Imielinski T, Swami AN. "Mining Association

Rules between Sets of Items in Large Databases." SIGMOD. June 1993, 22(2):207-16, pdf.

Agrawal R, Srikant R. "Fast Algorithms for Mining Association Rules", VLDB. Sep 12-15 1994, Chile, 487-99, pdf, ISBN 1-55860-153-8.

Mannila H, Toivonen H, Verkamo AI. "Efficient algorithms for discovering association rules." AAAI Workshop on Knowledge Discovery in Databases (SIGKDD). July 1994, Seattle, 181-92, ps.

Implementation of the algorithm in C# Retrieved from

"http://en.wikipedia.org/wiki/Apriori_algorithm"