Cheshulko / Apriori

Implementation of Apriori algorithm which used for determining relations among variables in datasets.

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