Click here to Skip to main content
12,547,806 members (33,850 online)
Click here to Skip to main content
Add your own
alternative version

Tagged as


147 bookmarked

Apriori Algorithm

, 10 Aug 2012 CPOL
Rate this:
Please Sign up or sign in to vote.
Implementation of the Apriori algorithm in C#.


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
Australia Australia
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.

You may also be interested in...


Comments and Discussions

GeneralMy vote of 5 Pin
manoj kumar choubey28-Mar-12 0:22
membermanoj kumar choubey28-Mar-12 0:22 

General General    News News    Suggestion Suggestion    Question Question    Bug Bug    Answer Answer    Joke Joke    Praise Praise    Rant Rant    Admin Admin   

Use Ctrl+Left/Right to switch messages, Ctrl+Up/Down to switch threads, Ctrl+Shift+Left/Right to switch pages.

| Advertise | Privacy | Terms of Use | Mobile
Web01 | 2.8.161021.1 | Last Updated 10 Aug 2012
Article Copyright 2010 by Omar Gameel Salem
Everything else Copyright © CodeProject, 1999-2016
Layout: fixed | fluid