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Python Implementation of Apriori Algorithm for finding Frequent sets and Association Rules
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.gitignore Ignore *.swp and *.pyc Jul 6, 2016
.travis.yml Use requirements.txt to keep track Python package list Jul 6, 2016
INTEGRATED-DATASET.csv First Commit Dec 5, 2011 Print itemsets sorted by support and confidence rules by confidence Jul 1, 2015
mit-license Adding MIT-License Aug 7, 2013
requirements.txt Use requirements.txt to keep track Python package list Jul 6, 2016
tesco.csv Add small dataset about buying transaction Jul 6, 2016 Test running Apriori to get items and rules as result Jul 6, 2016

Python Implementation of Apriori Algorithm

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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

  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 <>_


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


To run program with dataset

python -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



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