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.
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
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
- apriori.py
- INTEGRATED-DATASET.csv
- 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/>_
MIT-License