Python Implementation of Apriori Algorithm 
==========================================

List of files
-------------
1. apriori.py
2. INTEGRATED-DATASET.csv
3. README(this file)

The dataset is a copy of the “Online directory of certified businesses with a detailed profile” file from the Small Business Services (SBS) 
dataset in the `NYC Open Data Sets <http://nycopendata.socrata.com/>`_

Usage
-----
To run the program with dataset provided and default values for *minSupport* = 0.15 and *minConfidence* = 0.6

    python apriori.py -f INTEGRATED-DATASET.csv

To run program with dataset  

    python apriori.py -f INTEGRATED-DATASET.csv -s 0.17 -c 0.68

Best results are obtained for the following values of support and confidence:  

Support     : Between 0.1 and 0.2  

Confidence  : Between 0.5 and 0.7 

License
-------
MIT-License

-------

Python Implementation of Apriori Algorithm 
==========================================

[![Build Status](https://travis-ci.org/zkan/Apriori.svg?branch=master)](https://travis-ci.org/zkan/Apriori)

List of files
-------------
1. apriori.py
2. INTEGRATED-DATASET.csv
3. README(this file)

The dataset is a copy of the “Online directory of certified businesses with a detailed profile” file from the Small Business Services (SBS) 
dataset in the `NYC Open Data Sets <http://nycopendata.socrata.com/>`_

Usage
-----
To run the program with dataset provided and default values for *minSupport* = 0.15 and *minConfidence* = 0.6

    python apriori.py -f INTEGRATED-DATASET.csv

To run program with dataset  

    python apriori.py -f INTEGRATED-DATASET.csv -s 0.17 -c 0.68

Best results are obtained for the following values of support and confidence:  

Support     : Between 0.1 and 0.2  

Confidence  : Between 0.5 and 0.7 

License
-------
MIT-License

-------

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