Selecting the right interestingness measure for association patterns

By Pang-Ning Tan, Vipin Kumar, Jaideep Srivastava. Knowledge Discovery and Data Mining, 2002

1,050 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/0c05161edbbb2eb0cc095e010174a83929ae4364

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Citations in the corpus

Shrinkage observed-to-expected ratios for robust and transparent large-scale pattern discovery (2011, background): “The odds ratio has many desirable properties for a measure of association.(11) Specifically, it is variation independent of the marginal frequencies f(x) and f(y ), and thus applies equally well to common and rare events.”
Learning Accurate and Interpretable Classifiers Using Optimal Multi-Criteria Rules (2013, background): “Such criteria include important rule statistics [Tan et al. 2002], namely: support, confidence, added-value, and Yule’s Q.”
Mining significant graph patterns by leap search (2008, neutral): “[22] summarized around twenty-one interestingness measures.”
A Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part I (2014, background): “Similarly, a rule mining problem may optimize several rule interestingness measures such as support, confidence, comprehensibility, and lift [28] at the same time.”
Proceedings of the 5th International Conference on Image and Video Retrieval (2006, neutral): “Finding interesting item sets (and association rules) is a much discussed topic in the data mining literature [16].”
AMIE: association rule mining under incomplete evidence in ontological knowledge bases (2013, background): “[36] discusses a number of alternatives to measure the interestingness of a rule in general.”
Recent automatic text summarization techniques: a survey (2016, methodology): “Lift (Tan et al. 2002) measure is used for evaluating positive and negative correlations among the frequently occurring terms and it signifies the strength of association between a pair of terms.”
Discriminative Frequent Pattern Analysis for Effective Classification (2007, methodology): “In this paper, we use a variant of the Jaccard measure [18] to measure the redundancy between different features.”