Bayesian Association Rules Mining Algorithm

This repository contains the code for our FDS assignment on the Bayesian Association Rules Mining Algorithm.

Title: A Bayesian Association Rule Mining Algorithm (2013 IEEE International Conference on Systems, Man, and Cybernetics)

We will be using the Breast Cancer dataset for demonstration purposes. You can find the dataset here.

Instructions to Run the Code

The requirements are listed in the requirements.txt file. To install the requirements, run the following command:

pip install -r requirements.txt

The dataset is in the data folder. To preprocess the data, run the following commands:

python preprocess.py
bash convert.sh

To run the BAR algorithm, use the command:

python bar.py

To display the association rules and class association rules, run:

python main.py

The file bar.py generates association rules along with their support, confidence, Bayesian confidence, and lift values. The file apriori.py takes two arguments as input: the minimum support value and the minimum confidence value for the Apriori algorithm.

Dataset Information

• Number of Instances: 286
• Number of Attributes: 9 + the class attribute

Attribute Information:

1. Class: no-recurrence-events, recurrence-events
2. Age: 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90-99
3. Menopause: lt40, ge40, premeno
4. Tumor Size: 0-4, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59
5. Inv-Nodes: 0-2, 3-5, 6-8, 9-11, 12-14, 15-17, 18-20, 21-23, 24-26, 27-29, 30-32, 33-35, 36-39
6. Node Caps: yes, no
7. Deg-Malig: 1, 2, 3
8. Breast: left, right
9. Breast-Quad: left-up, left-low, right-up, right-low, central
10. Irradiat: yes, no

Missing Attribute Values:

• Attribute 6: 8 instances with missing values
• Attribute 9: 1 instance with missing value

Class Distribution:

• No-Recurrence-Events: 201 instances
• Recurrence-Events: 85 instances