Support Confidence Metrics constitute a statistical framework in Association Rule Mining used to quantify the reliability and frequency of itemset co-occurrences within large transactional databases. The theory relies on formal definitions where Support measures the relative frequency of an itemset, while Confidence evaluates the conditional probability of one item given another's presence as evidence. These metrics serve as fundamental filter parameters that define association rules without addressing rule lift or correlation directly.
Prerequisite chain context: requires Intra-class Correlation Coefficient (ICC) for Metric Variables.