In the context of unsupervised learning, conviction refers to a measure of the strength of association between variables in a dataset. It indicates how much stronger the presence of one variable is in relation to another, helping to identify patterns or clusters in data without labeled outcomes. This concept plays a crucial role in clustering algorithms and association rule mining, enabling analysts to derive meaningful insights from complex datasets.
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