Which items sell together? Association rule mining finds patterns like {Bread} → {Butter} using support and confidence. Slide the support threshold and watch Apriori prune the infrequent itemsets in real time.
Support = how often; Confidence = how reliable; Lift > 1 = positively correlated.
If an itemset is infrequent, then all of its supersets are also infrequent. This anti-monotone property lets Apriori prune huge numbers of candidates without counting them — the key to efficiency.
Six shopping baskets. Slide the minimum support — itemsets that meet it turn green (frequent); the rest are pruned. Notice how raising support shrinks the frequent set and, by Apriori, kills the bigger itemsets first.
From a frequent itemset we generate rules and score them. Pick a rule and see its support, confidence and lift, computed from the same six baskets.
A set of items appearing together in at least min-support transactions.
Iteratively generates candidate k-itemsets from frequent (k−1)-itemsets, counts support, prunes.
Join frequent (k−1)-itemsets; prune any whose (k−1)-subset is infrequent.
Scans the DB in two passes by partitioning it — any global frequent set is frequent in some partition.
Builds a compact FP-tree and mines frequent patterns without candidate generation — usually faster.
A frequent itemset with no frequent supersets.
A frequent itemset with no superset having the same support.
For each frequent itemset, form rules and keep those above min-confidence.