Like Apriori algorithm, FP-Growth is an association rule mining approach. The term FP in the name of this approach, is abbreviation of Frequent Pattern. FP-Growth uses a frequent pattern mining technique to build a tree of frequent patterns (FP-Tree), which can be used to extract association rules. Compared to Apriori, the FP-Growth approach is more efficient and it has better performance for rule mining in large datsets. For more information on the FP-Growth approach, you can refer to the related Wikipedia article, here.
In this post, we are going to share with you, the open-source implementation of FP-Growth association rule mining algorithm in MATLAB. 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.
[box type=”info”]Also the MATLAB implementation of Apriori association rule mining is available to download, in this link.[/box]
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