In data mining, conviction is a measure of the strength of an association rule, reflecting how much more likely the consequent is to occur when the antecedent occurs. It evaluates the reliability of a rule by comparing the expected frequency of the consequent when the antecedent is present to its actual frequency. A higher conviction value indicates a stronger relationship between the items, making it a vital metric in understanding associations and patterns in data.
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