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Frequent Itemset Mining

Apriori algorithm for discovering frequent itemsets for mining Boolean association rules.

Motivation : http://cse.iitkgp.ac.in/~bivasm/uc_notes/07apriori.pdf

Original Paper :

Rakesh Agrawal and Ramakrishnan Srikant Fast algorithms for mining association rules in large databases. Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, pages 487-499, Santiago, Chile, September 1994.

##Usage

The algorithm can be executed with (Both minimum support and minimum confidence lie between [0, 1]):

python apriori.py <data_set> <minimum_support> <minimum_confidence>

Example:

python apriori.py datasets/retail.csv 0.3 0.6

##Dataset:

retail.dat contains the (anonymized) retail market basket data from an anonymous Belgian retail store(Source: http://fimi.ua.ac.be/data/). Additionally, retail.dat was converted into retail.csv using dat2csv.py provided in the repository

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