FP-Growth

• FP-growth

Frequent Pattern Growth, is used as an analytical process that finds frequent patterns or associations from data sets. It greatly reduces the size of the itemset in the database by one simple principle which is if an itemset is frequent, then all of its subsets must also be frequent. It only scans the database twice and used a tree structure(FP-tree) to store all the information. The root represents null, each node represents an item, while the association of the nodes is the itemsets with the order maintained while forming the tree. The FP-tree is concise and is used to directly generating large itemsets. Once an FP-tree has been constructed, it uses a recursive divide-and-conquer approach to mine the frequent itemsets.

reference : https://towardsdatascience.com/fp-growth-frequent-pattern-generation-in-data-mining-with-python-implementation-244e561ab1c3

 

https://github.com/mellamonaranja/FP-Growth