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There Apriori algorithm has been implemented as Apriori.java (version 2).

A Java applet which combines DIC, Apriori and Probability Based Objected Interestingness Measures can be found here.

Note: Java 1.6.0_07 or newer

Download the following files:

Apriori.java: Simple implementation of the Apriori Itemset Generation algorithm.
Version 2: Apriori Itemset Generation algorithm that uses a hash tree.
config.txt: Consists of four lines.
Number of items
Number of transactions
Minimum support i ....etc

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What is Apriori?

Apriori is an algorithm for discovering frequent itemsets in transaction databases. It was proposed by Agrawal & Srikant (1993).

What is the input of the Apriori algorithm?

The input is a transaction database (aka binary context) and a threshold named minsup (a value between 0 and 100 %).

A transaction database is a set of transactions. Each transaction is a set of items. For example, consider the following transaction database. It contains 5 transactions (t1, t2, .., t5) and 5 items (1,2, 3, 4, 5). For example, the first t ....etc

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import java.io.*;
import java.util.*;

/** The class encapsulates an implementation of the Apriori algorithm
* to compute frequent itemsets.
*
* Datasets contains integers (>=0) separated by spaces, one transaction by line, e.g.
* 1 2 3
* 0 9
* 1 9
*
* Usage with the command line :
* $ java mining.Apriori fileName support
* $ java mining.Apriori /tmp/data.dat 0.8
* $ java mining.Apriori /tmp/data.dat 0.8 > frequent-itemsets.txt
*
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Introduction
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).

Overview
The whole point of the algorithm (and data mining, in general) is ....etc

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