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