Interestingness measures for data mining: A survey

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Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, background): “The best-known quality measures for rules are support and confidence [121], [122].”
Learning customized and optimized lists of rules with mathematical programming (2018, methodology): “Hamilton [24], and example methods are those of Simon et al [60], Fawcett [20].”
ForEx++: A New Framework for Knowledge Discovery from Decision Forests (2017, methodology): “ In addition to accuracy and coverage, ForEx++ considers rule length as concise rules are said to be more comprehensible (Geng and Hamilton (2006)).”
Frequent item set mining (2012, background): “Overviews of ranking and selection measures can be found in Refs 64–66.”
Searching for rules to detect defective modules: A subgroup discovery approach (2012, background): “When li for an attribute ai is equal to its minimum value (li = min(ai)) such constrain will not be part of the rule and equally when the ui value for an attribute is equal to its maximum value (ui = max(ai)).”