/Apriori

Python Implementation of Apriori Algorithm for finding Frequent sets and Association Rules

Primary LanguagePythonMIT LicenseMIT

Python Implementation of Apriori Algorithm

Build Status

The code attempts to implement the following paper:

Agrawal, Rakesh, and Ramakrishnan Srikant. "Fast algorithms for mining association rules." Proc. 20th int. conf. very large data bases, VLDB. Vol. 1215. 1994.

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