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asaini / Apriori

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

Set up

Open in Streamlit Build Status

Edit without local environment setup

Open in Gitpod

Acknowledgements

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.

Interactive Streamlit App

To view a live interactive app, and play with the input values, please click here. This app was built using Streamlit 😎, the source code for the app can be found here

Running the Streamlit app locally

To run the interactive Streamlit app with dataset 

$ pip3 install -r requirements.txt
$ streamlit run streamlit_app.py

CLI 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

Datasets

INTEGRATED-DATASET.csv

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/>_

tesco.csv

Toy dataset of items from shopping cart

License

MIT-License