This assignment was to implement FP-Growth. The assignment was completed in python version python 3.9.0. FP-Growth is an algorithm for frequent pattern mining of data sets. Its benefit in comparison to Apriori is that it holds all transaction data in memory in a tree structure that allows for a level of compression for memory saving.

The goal was to implement FP-Growth as efficiently as possible. In my algorithm I construct the Global FP tree and rather then generating the temporary search trees I simply navigate the global search tree repeatedly with python dictionary "explorationtable" that keeps the context of the particular search iteration.

The Data is expected to be as follows:
4
1 3 1 3 4
2 3 2 3 5
3 4 1 2 3 5
4 2 2 5

line 1 holds number of transactions. Transaction lines are tab separated with first column be id, second being number of items and third column being space separated item list.

In the "Assignment_2" directory there are three python files titled "FPGrowth.py", "FPGrowthMain.py", and Node.py. FPGrowth.py is a class file that holds the function for my FP-Growth implementation. FPGrowthMain.py will the file main file for running FP-Growth in command line. Node.py is a simple Node class for tree construction.

To run FPGrowthMain in command line simply type in:

python3 FPGrowthMain.py -f [FileDirectory] -m [MinimumSupport] -o [OutputFileDirectory]

FileDirectory can be any file of valid format.

MinimumSupport must be an integer between 0-100.

OutputDirectory directory for output.