MBA involves analyzing customer transaction data to identify which products are purchased together in the same basket or transaction. The analysis is typically done by looking at the frequency of co-occurrence of items in the transaction data, and then applying statistical techniques to identify which items are most frequently purchased together.
The output of MBA is typically a set of rules or associations between products. For example, "customers who buy product A are 80% likely to also buy product B". These rules can be used to optimize product placement, create targeted promotions, and improve cross-selling strategies.
MBA is commonly used in combination with other data mining techniques such as clustering and segmentation to gain deeper insights into customer behavior and preferences.