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dc.contributor.authorRudin, Cynthia
dc.contributor.authorLetham, Benjamin
dc.contributor.authorMadigan, David
dc.date.accessioned2014-02-24T16:27:19Z
dc.date.available2014-02-24T16:27:19Z
dc.date.issued2013-11
dc.date.submitted2013-07
dc.identifier.issn1532-4435
dc.identifier.issn1533-7928
dc.identifier.urihttp://hdl.handle.net/1721.1/85071
dc.description.abstractWe present a theoretical analysis for prediction algorithms based on association rules. As part of this analysis, we introduce a problem for which rules are particularly natural, called “sequential event prediction." In sequential event prediction, events in a sequence are revealed one by one, and the goal is to determine which event will next be revealed. The training set is a collection of past sequences of events. An example application is to predict which item will next be placed into a customer's online shopping cart, given his/her past purchases. In the context of this problem, algorithms based on association rules have distinct advantages over classical statistical and machine learning methods: they look at correlations based on subsets of co-occurring past events (items a and b imply item c), they can be applied to the sequential event prediction problem in a natural way, they can potentially handle the “cold start" problem where the training set is small, and they yield interpretable predictions. In this work, we present two algorithms that incorporate association rules. These algorithms can be used both for sequential event prediction and for supervised classification, and they are simple enough that they can possibly be understood by users, customers, patients, managers, etc. We provide generalization guarantees on these algorithms based on algorithmic stability analysis from statistical learning theory. We include a discussion of the strict minimum support threshold often used in association rule mining, and introduce an “adjusted confidence" measure that provides a weaker minimum support condition that has advantages over the strict minimum support. The paper brings together ideas from statistical learning theory, association rule mining and Bayesian analysis.en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (Grant IIS-1053407)en_US
dc.language.isoen_US
dc.publisherAssociation for Computing Machinery (ACM)en_US
dc.relation.isversionofhttp://jmlr.org/papers/volume14/rudin13a/rudin13a.pdfen_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceAssociation for Computing Machineryen_US
dc.titleLearning Theory Analysis for Association Rules and Sequential Event Predictionen_US
dc.typeArticleen_US
dc.identifier.citationRudin, Cynthia, Benjamin Letham, and David Madigan. "Learning Theory Analysis for Association Rules and Sequential Event Prediction." Journal of Machine Learning Research 14 (2013): 3441-3492.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Operations Research Centeren_US
dc.contributor.departmentSloan School of Managementen_US
dc.contributor.mitauthorRudin, Cynthiaen_US
dc.contributor.mitauthorLetham, Benjaminen_US
dc.relation.journalJournal of Machine Learning Researchen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsRudin, Cynthia; Letham, Benjamin; Madigan, Daviden_US
mit.licensePUBLISHER_POLICYen_US
mit.metadata.statusComplete


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