DOI: 10.1017/S000000000000000 Printed in the United Kingdom A Survey of Interestingness Measures for Knowledge Discovery

Citations in the corpus

• Learning customized and optimized lists of rules with mathematical programming (2018, background): “There is a large body of literature on interestingness measures for rules, such as lift, conviction, Laplace, and gain, and reviews of interestingness measures are given by Tan and Kumar [62], McGarry [44], Geng and Hamilton [24], and example methods are those of Simon et al [60], Fawcett [20].”
• ORC: Ordered Rules for ClassificationA Discrete Optimization Approach to Associative Classification (2012, background): “There is a large body of literature on interestingness measures, such as lift, conviction, Laplace, and gain [review articles include those of Tan and Kumar, 2000, McGarry, 2005, Geng and Hamilton, 2006].”
• User-oriented Assessment of Classification Model Understandability (2011, unknown): “The majority of previous work has concentrated on producing accurate models as well as the assessment of other aspects such as running time and space used, with a slight consideration for ranking the value of patterns and evaluating effectiveness from a user point of view [1][13][14].”
• Learning theory analysis for association rules and sequential event prediction (2013, background): “Many “interestingness” measures have been proposed in the literature (see Tan et al., 2002; Geng and Hamilton, 2007; McGarry, 2005).”