The building blocks of the algorithm are the items that are found in any given transaction. Each transaction could have one or more items in it. The items that form a transaction are called an itemset. An example of a transaction is an invoice.
Given the transactions dataset, the objective is to find the items in data that are associated with each other. Association is measured as frequency of the occurrence of the items in the same context. For example, purchasing one product when another product is purchased represents an association rule. The association rule detects the common usage of items.
More formally, we can define association-rule mining as, given a set of items I = {I1,I2,..Im} and database of transactions ...
