https://github.com/cudavailable/association-rule-mining
A simple implementation of Apriori algotithem and mining strong association rules on supermarket shopping dataset
https://github.com/cudavailable/association-rule-mining
apriori-algorithm data-mine
Last synced: 3 months ago
JSON representation
A simple implementation of Apriori algotithem and mining strong association rules on supermarket shopping dataset
- Host: GitHub
- URL: https://github.com/cudavailable/association-rule-mining
- Owner: cudavailable
- Created: 2024-11-06T07:56:04.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-11-07T05:23:10.000Z (7 months ago)
- Last Synced: 2025-01-26T04:14:03.034Z (5 months ago)
- Topics: apriori-algorithm, data-mine
- Language: Python
- Homepage:
- Size: 394 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## File Descriptions:
- Apriori.py : loads dataset and mines strong association rules based on Apriori
- logger.py : definition of a class 'Logger'
- Datebase : dataset (e.g. supermarket shopping dataset)
- log : includes a series of log files keeping the result of Apriori under different experimental configurations
## Conclusions:
1. Min_Conf remains unchanged. The larger Min_Sup is, the fewer frequent itemsets are mined, and the running time of the Apriori algorithm is relatively reduced.
2. Min_Sup remains unchanged. The larger Min_Conf is, the fewer strong association rules are mined. There is no specific change pattern in the running time of the Apriori algorithm.
3. When Min_Sup=0.3，Min_Conf=0.95, we still have:
- 1 ['常温熟食类', '进口食品'] -> ['饮料'] Conf=0.969957 Sup=0.302949
- 2 ['散装休闲食品', '进口食品'] -> ['饮料'] Conf=0.950207 Sup=0.306971
- 3 ['常温熟食类', '散装休闲食品'] -> ['饮料'] Conf=0.951020 Sup=0.312332
## Reference:
https://github.com/ZbWeR/Association-rule-mining