IB Computer Science Topic A4.3.5: Learning Techniques Using the Association Rule Used in Large Data Sets Key Definitions

A method for association rule learning that first finds every itemset meeting a minimum support, then builds rules from those itemsets and keeps the ones meeting a minimum confidence. It prunes the search using the property that any set containing an infrequent itemset must itself be infrequent.

An if-then statement reporting that two sets of items tend to appear in the same transaction, written $\{\text{antecedent}\} \Rightarrow \{\text{consequent}\}$. It records co-occurrence in the data and makes no claim about cause.

For an association rule, the share of the transactions containing the antecedent that also contain the consequent. It equals the support of the whole itemset divided by the support of the antecedent alone.

The confidence of an association rule divided by the support of its consequent. A lift above 1 means the antecedent raises the chance of the consequent, a lift of 1 means the two are independent, and a lift below 1 means the antecedent lowers it.

In association rule learning, the share of all transactions that contain every item in a given itemset. Support of an itemset equals the number of transactions holding all of its items divided by the total number of transactions.