This module provides a pure Python implementation of the FP-growth algorithm for finding frequent itemsets. FP-growth exploits an (often-valid) assumption that many transactions will have items in common to build a prefix tree. If the assumption holds true, this tree produces a compact representation of the actual transactions and is used to generate itemsets much faster than Apriori can.

This Project is fork from enaeseth's open source Project Python FP-Growth. Seace that the project haven't been maintained for several years, I decide to fork a new project for python3's version.

After downloading and extracting the package, install the module by running python setup.py install from within the extracted package directory. (If you encounter errors, you may need to run setup with elevated permissions: sudo python setup.py install.)

Usage of the module is very simple. Assuming you have some iterable of transactions (which are themselves iterables of items) called transactions and an integer minimum support value minsup, you can find the frequent itemsets in your transactions with the following code:

from fp_growth import find_frequent_itemsets

for itemset in find_frequent_itemsets(transactions, minsup):
    print itemset

Note that find_frequent_itemsets returns a generator of itemsets, not a greedily-populated list. Each item must be hashable (i.e., it must be valid as a member of a dictionary or a set).

Once installed, the module can also be used as a stand-alone script. It will read a list of transactions formatted as a CSV file. (An example of such a file in included in the examples directory.)

python3 -m fp_growth -s {minimum support} {path to CSV file}

For example, to find the itemsets with support ≥ 4 in the included example file:

python3 -m fp_growth -s 4 examples/tsk.csv

Also, sopport can be a support rate, for example:

python3 -m fp_growth -s 0.3 examples/tsk.csv

You can find association rules as well, as sample

python3 -m fp_growth -f rule -c 0.4 -s 4 examples/tsk.csv

We used -f to decide what you want to find, association rule or frequency itemset

All parameter:

• -s: minimum support (count or rate is fine)
• -c: minimum confidence
• -f: problem solving (freq or rule)

The following references were used as source descriptions of the algorithm:

• Tan, Pang-Ning, Michael Steinbach, and Vipin Kumar. Introduction to Data Mining. 1st ed. Boston: Pearson / Addison Wesley, 2006. (pp. 363-370)
• Han, Jiawei, Jian Pei, and Yiwen Yin. "Mining Frequent Patterns without Candidate Generation." Proceedings of the 2000 ACM SIGMOD international conference on Management of data, 2000.

The example data included in tsk.csv comes from the section in Introduction to Data Mining.

The python3-Fp-growth package is made available under the terms of the MIT License.

Copyright © 2018 C.A.Lee