apriori-agorithm-python

An Effectively Python Implementation of Apriori Algorithm for Finding Frequent sets and Association Rules

List of files

1. data/transaction.csv: input file
2. apriori.py: define a class Apriori
3. test_apriori_command_line.py: test the apriori algorithm

Dataset

Your should input path of a csv file, which may seems like:

Bread,Milk 
Bread,Diapers,Beer,Eggs
Milk,Diapers,Beer,Cola
Bread,Milk,Diapers,Beer
Bread,Milk,Diapers,Cola
Bread,Milk
Bread,Cola,Beer,Milk
Milk,Bread,Beer,Cola
Bread,Milk,Diapers,Beer
Bread,Beer,Diapers,Diapers

Each line is a transaction, seperated by comma (,).

Usage

To run the program with dataset provided (in ./data/) and mininum support = 0.12, mininum confidence = 0.5 and return rules for Bread:

python test_apriori_command_line.py -f ./data/transaction.csv -s 0.20 -c 0.50 -r Bread

which produce results below:

frequent 1-term set:
--------------------
['Diapers']
['Beer']
['Cola']
['Bread']
['Milk']

frequent 2-term set:
--------------------
['Diapers', 'Beer']
['Milk', 'Bread']
['Diapers', 'Bread']
['Diapers', 'Milk']
['Bread', 'Cola']
['Bread', 'Beer']
['Milk', 'Cola']
['Cola', 'Beer']
['Milk', 'Beer']

frequent 3-term set:
--------------------
['Milk', 'Cola', 'Beer']
['Milk', 'Bread', 'Cola']
['Milk', 'Bread', 'Beer']
['Diapers', 'Milk', 'Bread']
['Diapers', 'Bread', 'Beer']
['Diapers', 'Milk', 'Beer']

==============================
rules refer to ['Bread']
['Milk'] -> ['Bread']: 0.8749999999999999
['Diapers'] -> ['Bread']: 0.8333333333333334
['Cola'] -> ['Bread']: 0.7499999999999999
['Beer'] -> ['Bread']: 0.8571428571428572
['Milk', 'Cola'] -> ['Bread']: 0.7499999999999999
['Milk', 'Beer'] -> ['Bread']: 0.8
['Diapers', 'Milk'] -> ['Bread']: 0.7499999999999999
['Diapers', 'Beer'] -> ['Bread']: 0.8

Reference

Agrawal R, Srikant R. Fast algorithms for mining association rules[C]//Proc. 20th int. conf. very large data bases, VLDB. 1994, 1215: 487-499.

License

MIT-License