The standard model for association-rule mining involves a set of “items” and a set of “baskets.” The baskets contain items that some customer has purchased at the same time. The problem is to find pairs, or perhaps larger sets, of items that frequently appear together in baskets. We mention the principal approaches to efficient, large-scale discovery of the frequent itemsets, including the a-priori algorithm, improvements using hashing, and one- and two-pass probabilistic algorithms for finding frequent itemsets. We then turn to techniques for finding highly corre- lated, but infrequent, pairs of items. These notes were written for CS345 at Stanford University and are reprinted by permission of the author. http://www-db.stanford.edu/~ullman/mining/mining.html gives you access to the entire set of notes, including additional citations and on-line links.