Mining Association Rules with Apriori and FPGrowth Algorithm
Here, comparision is done between Apriori Algorithm from Apyori and MlxTend Libraries and FPGrowth Algorithm from pyfpgrowth Library. If we compare, then Apriori is based on candidate generation feature and FP-Growth is not based on candidate generation feature. Apriori is quite simple, easy and can be efficiently implemented, whereas FPGrowth is complex and at the same time uses very less memory as compared to Apriori because of complex tree structure formation. But i can say that efficiency of any algorithm is based on your requirements, datasets, and system supportability.
In this repository, there are three python files(.ipynb) where both the algorithms are implemented with 3 different libraries so that you can compare it properly by loading ur datasets. After importing the modules, give the absolute path of your dataset in place of ../. Execute cell by cell to get proper idea.