Empirical bayes screening for multi-item associations

By William DuMouchel, Daryl Pregibon. Knowledge Discovery and Data Mining, 2001

318 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

View Paper: https://www.semanticscholar.org/paper/bf3a8dfa1157ec1608eec9a6d2e73dc5971153f9

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Citations in the corpus

Learning theory analysis for association rules and sequential event prediction (2013, background): “Most prior work relies on the strong requirement of the minimum support threshold; some exceptions include the works of Li et al. (1999); Koh (2008) and DuMouchel and Pregibon (2001).”
Bayesian Hierarchical Rule Modeling for Predicting Medical Conditions (2012, unknown): “DuMouchel and Pregibon (2001) deal with the identification of interesting itemsets (rather than identification of rules).”
Sequential Event Prediction with Association Rules (2011, unknown): “In terms of Bayesian analysis, DuMouchel and Pregibon (2001) present a Bayesian approach to the identifica- tion of interesting itemsets.”
Use of Screening Algorithms and Computer Systems to Efficiently Signal Higher-Than-Expected Combinations of Drugs and Events in the US FDA’s Spontaneous Reports Database (2002, methodology): “This subset can be detected by reviewers using current methods or by using the MGPS algorithm[4] and some specific threshold level.”
Selecting the right objective measure for association analysis (2004, methodology): “DuMouchel has recently proposed a statistical correction to I for small sample sizes, using an empirical Bayes technique [26].”
Shrinkage observed-to-expected ratios for robust and transparent large-scale pattern discovery (2011, methodology): “For example, the practical relevance of highlighted patterns might in some applications be better measured in terms of absolute differences between the observed and expected numbers of events.(6) Confounding is a fundamental challenge in the analysis of observational data.”
Quantitative signal detection using spontaneous ADR reporting (2009, methodology): “The GPS as originally developed and PRR were reported based on combinations, 14 but MGPS 47 and IC using number of cases.”
Personalizing LinkedIn Feed (2015, methodology): “( We assume that S ik | E ik , g ik follows Gamma-Poission distribution Poisson assumption is reasonable and has been widely used in rare event estimations [15].”
A Hierarchical Model for Association Rule Mining of Sequential Events: An Approach to Automated Medical Symptom Prediction (2011, unknown): “DuMouchel and Pregibon (2001) presented a Bayesian analysis of association rules.”