A simple python implplementation of Apriori Algorithm for frequent item set mining and association rule learning over relational databases and dataframes.
Here I aim to implement an impllroved version of the algorithm i.e. AprioriTID inspired from Agarwal,Srikant et al. [1]

UCI Machine Learning Repository Bag of Words Dataset (http://archive.ics.uci.edu/ml/datasets/Bag+of+Words) by David Newman contains three collection of text documents

• Enron Emails - It contains data from about 150 users, mostly senior management of Enron.
• NIPS papers - Contains data from paoers appearing in NIPS conference
• KOS blog entries - Data of KAS blog entries, predominantly stored info about political news

• Downlowad the data from the link above.
• Clone the repository to your local PC.
• To extract the required data run the following command (see main.py for args help)

python main.py -d "data/docword.kos.txt" -v "data/vocab.kos.txt" -k 5 -ms 0.25 -o True

KOS dataset was passed through apriori algorithm multiple times with minimum support of 0.1, 0.2, 0.25 and 0.3 whereas NIPS dataset had minumum support of 0.4, 0.45, 0.5 and 0.6.
Some of the interesting frequent itemsets in KOS datasets include - {'create', 'democrats', 'war'}, {'bush', 'general', 'republicans', 'split'} whereas NIPS data had {'abstract', 'algorithm', 'approach', 'information', 'neural'} and {'abstract', 'application', 'input', 'set'} with word abstract being present in all the frequent itemset which is expected since all the documents of NIPS data contains word abstract. As we kept increasing minimum support and length of itemset both datasets followed a rather characteristic trend in terms of number of frequent itemsets generated and the time taekn to generate those which is shown in the graphs below.

KOS dataset	NIPS dataset

[1] Fast algorithms for mining association rules,1994, Agrawal, Rakesh and Srikant, Ramakrishnan and others