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Apriori Algorithm

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4.91/5 (84 votes)
10 Aug 2012CPOL2 min read
Implementation of the Apriori algorithm in C#.

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In 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).

Other algorithms are designed for finding association rules in data having no transactions (Winepi and Minepi), or having no timestamps (DNA sequencing).


The whole point of the algorithm (and data mining, in general) is to extract useful information from large amounts of data. For example, the information that a customer who purchases a keyboard also tends to buy a mouse at the same time is acquired from the association rule below:

Support: The percentage of task-relevant data transactions for which the pattern is true.

Support (Keyboard -> Mouse) = AprioriAlgorithm/eq_1.JPG

Confidence: The measure of certainty or trustworthiness associated with each discovered pattern.

Confidence (Keyboard -> Mouse) = AprioriAlgorithm/eq_2.JPG

The algorithm aims to find the rules which satisfy both a minimum support threshold and a minimum confidence threshold (Strong Rules).

  • Item: article in the basket.
  • Itemset: a group of items purchased together in a single transaction.

How Apriori Works

  1. Find all frequent itemsets:
    • Get frequent items:
      • Items whose occurrence in database is greater than or equal to the threshold.
    • Get frequent itemsets:
      • Generate candidates from frequent items.
      • Prune the results to find the frequent itemsets.
  2. Generate strong association rules from frequent itemsets
    • Rules which satisfy the and min.confidence threshold.

High Level Design


Low Level Design



A database has five transactions. Let the min sup = 50% and min con f = 80%.



Step 1: Find all Frequent Itemsets


Frequent Itemsets

{A}   {B}   {C}   {E}   {A C}   {B C}   {B E}   {C E}   {B C E}

Step 2: Generate strong association rules from the frequent itemsets



Closed Itemset: support of all parents are not equal to the support of the itemset.

Maximal Itemset: all parents of that itemset must be infrequent.

Keep in mind:



Itemset {c} is closed as support of parents (supersets) {A C}:2, {B C}:2, {C D}:1, {C E}:2 not equal support of {c}:3.

And the same for {A C}, {B E} & {B C E}.

Itemset {A C} is maximal as all parents (supersets) {A B C}, {A C D}, {A C E} are infrequent.

And the same for {B C E}.


This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)


About the Author

Omar Gameel Salem
Software Developer
Egypt Egypt
Enthusiastic programmer/researcher, passionate to learn new technologies, interested in problem solving, data structures, algorithms, AI, machine learning and nlp.

Amateur guitarist/ keyboardist, squash player.

Comments and Discussions

GeneralThanks Pin
himanshu21p6-Jan-12 19:37
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QuestionMy 5 Pin
theanil6-Jan-12 9:39
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QuestionVisual basics Pin
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AnswerRe: Visual basics Pin
Jason Vogel5-Jul-12 8:22
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Generalmy vote of 5 Pin
shehbazshkh21-Nov-11 23:29
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GeneralMy vote of 5 Pin
BillWoodruff6-Nov-11 22:44
mveBillWoodruff6-Nov-11 22:44 
QuestionDoes this code only work for single character ? Pin
haem savla15-Oct-11 6:27
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Questionproblem Pin
abarna1210-Oct-11 20:00
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AnswerRe: problem Pin
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professionalOmar Gameel Salem10-Oct-11 23:47 
AnswerRe: problem Pin
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Questionerror i got.. Pin
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GeneralRe: error i got.. Pin
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GeneralRe: error i got.. Pin
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Questionapriori algorithm Pin
salv0327-Sep-11 7:03
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GeneralMy vote of 5 Pin
Milad Rk6-Sep-11 22:19
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QuestionHelp on modifications Pin
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GeneralRe: Help on modifications Pin
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Questionrevision of borderset Pin
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QuestionInput help in C++ for prices' algorithm(similar to apriori) Pin
adiiscool327-May-11 22:54
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