Finding frequent itemsets is a type of counting activity. But unlike producing a simple tally of items we observe in a dataset (today we sold 80 carrots and 100 tomatoes), finding frequent itemsets is slightly different. Specifically, to find frequent itemsets we look for co-occurring sets of items within some larger group. These larger groups are sometimes imagined as supermarket transactions or shopping baskets, and the entire exercise is sometimes called market basket analysis. Staying with the supermarket analogy, the items co-occurring within those baskets are sometimes imagined to be combinations of products purchased at the supermarket. For example, given a set of supermarket transactions or baskets, we might be interested in whether the itemset of {carrots, tomatoes} occurs more frequently in baskets than does the {cucumbers, lemons} itemset.
The purpose of frequent itemset mining is to make interesting discoveries of co-occurring items within a set of...
