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This article focuses on subjective methods of evaluation of discovered patterns in data that depend not only on the structure of the pattern and the data but also on the user who examines the pattern. The article considers such subjective measures of interestingness as unexpectedness, actionability, template-based measures, including data mining queries, pattern templates, and meta rules, and background knowledge measures. Finally, it describes how these different interestingness measures can be integrated into one common approach.