Association Rule Mining

Association rule mining is the data mining process of finding the rules that may govern associations and causal objects between sets of items.

So in a given transaction with multiple items, it tries to find the rules that govern how or why such items are often bought together. For example, peanut butter and jelly are often bought together because a lot of people like to make PB&J sandwiches.

Also surprisingly, diapers and beer are bought together because, as it turns out, that dads are often tasked to do the shopping while the moms are left with the baby.

The main applications of association rule mining:

• Basket data analysis - is to analyze the association of purchased items in a single basket or single purchase as per the examples given above.
• Cross marketing - is to work with other businesses that complement your own, not competitors. For example, vehicle dealerships and manufacturers have cross marketing campaigns with oil and gas companies for obvious reasons.
• Catalog design - the selection of items in a business’ catalog are often designed to complement each other so that buying one item will lead to buying of another. So these items are often complements or very related.