Apriori algorithm

Overview

Apriori algorithm is used to find relevant association rules over transactions dataset.

Input

The input file is csv-file contained transactions.

Outlook	Temperature	Humidity	Windy	PlayTennis
sunny	hot	high	FALSE	N
sunny	hot	high	TRUE	N
overcast	hot	high	FALSE	P
rain	mild	high	FALSE	P
rain	cool	normal	FALSE	P
rain	cool	normal	TRUE	N
overcast	cool	normal	TRUE	P
sunny	mild	high	FALSE	N
sunny	cool	normal	FALSE	P
rain	mild	normal	FALSE	P
sunny	mild	normal	TRUE	P
overcast	mild	high	TRUE	P
overcast	hot	normal	FALSE	P
rain	mild	high	TRUE	N

Output

The output file contains n-term sets and 2-set rules.

Support=0.25
Confidence=0.25
1-term set:
[('Temperature', 'cool')]
[('Outlook', 'overcast')]
...
2-term set:
[('PlayTennis', 'P'), ('Outlook', 'overcast')]
[('PlayTennis', 'P'), ('Windy', 'FALSE')]
...
3-term set:
[('PlayTennis', 'P'), ('Humidity', 'normal'), ('Windy', 'FALSE')]
...
2-set rules:
Rule#1: {Humidity=normal} => {Temperature=cool}(Support=0.29, Confidence=0.57)
Rule#2: {PlayTennis=P} => {Windy=FALSE}(Support=0.43, Confidence=0.67)
Rule#3: {Humidity=high} => {Windy=FALSE}(Support=0.29, Confidence=0.57)
...

Build and run

To run use (Python3 needed)

./RuleMining.py -s <min_support> -c <min_confidence> -i <input_file.csv> -o <output_file.txt>

For instance,

./RuleMining.py -s 0.25 -c 0.40 -i Play_Tennis_Data_Set.csv -o Rules_out.txt

License

MIT