Introduction - If you have any usage issues, please Google them yourself
Identifying frequent item-sets can be useful in many ways. In the case of a supermarket, knowledge of which items are often purchased together could be used for product placement, targeted marketing, and so on. Additionally, once all frequent items sets have been identified, these item-sets could be analyzed further to extract rules such as, “if customers purchase both apples and bread, then there’s high probability they will also purchase lettuce and milk.” This overall process of first extracting frequent item-sets and then harvesting if-then rules is called association rule learning.