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KAPMiner: Mining Ordered Association Rules with Constraints
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2017 (English)In: Advances in Intelligent Data Analysis XVI: Proceedings / [ed] Niall Adams, Allan Tucker, David Weston, 2017, p. 149-161Conference paper, Published paper (Refereed)
Abstract [en]

We study the problem of mining ordered association rules from event sequences. Ordered association rules differ from regular association rules in that the events occurring in the antecedent (left hand side) of the rule are temporally constrained to occur strictly before the events in the consequent (right hand side). We argue that such constraints can provide more meaningful rules in particular application domains, such as health care. The importance and interestingness of the extracted rules are quantified by adapting existing rule mining metrics. Our experimental evaluation on real data sets demonstrates the descriptive power of ordered association rules against ordinary association rules.

Place, publisher, year, edition, pages
2017. p. 149-161
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10584
National Category
Computer Sciences
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-149268DOI: 10.1007/978-3-319-68765-0_13ISBN: 978-3-319-68764-3 (print)ISBN: 978-3-319-68765-0 (electronic)OAI: oai:DiVA.org:su-149268DiVA, id: diva2:1159984
Conference
16th International Symposium, IDA 2017, London, UK, October 26–28, 2017
Available from: 2017-11-24 Created: 2017-11-24 Last updated: 2018-01-13Bibliographically approved

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  • apa
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  • de-DE
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