In the context of association rule mining, conviction is a measure of the strength of an implication between two items in a dataset. It provides insight into how much more likely one item is to occur in the presence of another, helping analysts understand the relationship between variables. High conviction indicates a strong association, suggesting that if one item occurs, the other is likely to occur as well, while low conviction suggests a weaker connection.
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