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KAPMiner: Mining Ordered Association Rules with Constraints
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
2017 (engelsk)Inngår i: Advances in Intelligent Data Analysis XVI: Proceedings / [ed] Niall Adams, Allan Tucker, David Weston, 2017, s. 149-161Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
2017. s. 149-161
Serie
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10584
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
URN: urn:nbn:se:su:diva-149268DOI: 10.1007/978-3-319-68765-0_13ISBN: 978-3-319-68764-3 (tryckt)ISBN: 978-3-319-68765-0 (digital)OAI: oai:DiVA.org:su-149268DiVA, id: diva2:1159984
Konferanse
16th International Symposium, IDA 2017, London, UK, October 26–28, 2017
Tilgjengelig fra: 2017-11-24 Laget: 2017-11-24 Sist oppdatert: 2018-01-13bibliografisk kontrollert

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