Transcript Slide 1 - Department of Computer Science

Apriori Algorithm Review for
Finals.
SE 157B, Spring Semester 2007
Professor Lee
By
Gaurang Negandhi
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Overview
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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
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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.)
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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 Bought
1
Shoes, Shirt, Jacket
2
Shoes,Jacket
3
Shoes, Jeans
4
Shirt, Sweatshirt
If the minimum support is 50%, then {Shoes, Jacket} is the only 2itemset that satisfies the minimum support.
Frequent Itemset
{Shoes}
{Shirt}
{Jacket}
{Shoes, Jacket}
Support
75%
50%
50%
50%
If the minimum confidence is 50%, then the only two rules generated from this 2itemset, 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
Min support =50%
Database D
TID
100
200
300
400
itemset sup.
C1
{1}
2
{2}
3
Scan D
{3}
3
{4}
1
{5}
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Items
134
235
1235
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C2 itemset sup
L2 itemset sup
2
2
3
2
{1
{1
{1
{2
{2
{3
C3 itemset
{2 3 5}
Scan D
{1 3}
{2 3}
{2 5}
{3 5}
2}
3}
5}
3}
5}
5}
1
2
1
2
3
2
L1 itemset sup.
{1}
{2}
{3}
{5}
2
3
3
3
C2 itemset
{1 2}
Scan D
L3 itemset sup
{2 3 5} 2
{1
{1
{2
{2
{3
3}
5}
3}
5}
5}
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Pseudo Code for Apriori
Algorithm
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Apriori
Advantages/Disadvantages
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Advantages
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Uses large itemset property
Easily parallelized
Easy to implement
Disadvantages
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Assumes transaction database is memory
resident.
Requires many database scans.
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Summary
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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
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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 155860-153-8.
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"
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