Apriori Association Rule Mining in MATLAB

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Association Rule Mining is a common task in the field of Data Mining, involving the recognition of frequent patterns, usually in transactional databases. For example, discovering a rule like {bread, butter} → {milk} in a sales dataset is a result of association rule mining, and indicates that if a customer buys bread and butter, it is likely that they will buy milk too. For more information about association rule mining, you can refer to the related article in Wikipedia (here).

One of the basic methods of dealing with association rule mining problems, is the Apriori algorithm. It proceeds by identifying frequent individual items in the transactional dataset and extending them to larger and larger (more general) item-sets as long as those item-sets appear sufficiently often in the database, as valuable and useful rules. You can read more on Apriori algorithm in Wikipedia (here).

In this post, we are going to share with you, MATLAB implementation of Apriori association rule mining algorithm. The algorithm is implemented in a structured manner and if you are familiar with MATLAB programming language, you will find it easy, to use the codes in your research projects.

Also the MATLAB implementation of FP-Growth, another association rule mining algorithm, is available to download in this link.