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The Apriori algorithm calculates rules that express probabilistic relationships between items in frequent itemsets For example, a rule derived from frequent itemsets containing A, B, and C might state that if A and B are included in a transaction, then C is likely to also be included.
An association rule is of the form IF antecedent THEN consequent. An association rule states that an item or group of items, the antecendent, implies the presence of another item, the consequent, with some probability. Unlike decision tree rules, which predict a target, association rules simply express correlation.
Association rules have confidence and support:
Confidence of an association rule indicates the probability of both the antecedent and the consequent appearing in the same transaction. Confidence is the conditional probability that the consequent occurs given the occurrence of the antecedent. In other words, confidence is the ratio of the rule support to the number of transactions that include the antecedent.
Support of an association rule indicates how frequently the items in the rule occur together. Support is the ratio of transactions that include all the items in the antecedent and consequent to the number of total transactions.