Apriori [1] is an algorithm for frequent item set mining and association rule learning over transactional databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database. The frequent item sets determined by Apriori can be used to determine association rules which highlight general trends in the database: this has applications in domains such as market basket analysis.
The completion date of this project : May 2017
The time of publication in Github : 13 November 2018
You should first specify how much trasaction and itemset you want to create in order to setup a random data matrix
The data matrix :
The liste of the Frequent Itemset :
The liste of the rules :
The apriori algorithm was implemented using php, you can find it at : Pages/PHP/Apriori.class.php
The interface is powered by bootstrap.
[1]: Rakesh Agrawal and Ramakrishnan Srikant "Fast algorithms for mining association rules" (http://www.vldb.org/conf/1994/P487.PDF)