Show simple item record

Files in this item

Thumbnail

Item metadata

dc.contributor.authorKocak, Gokberk
dc.contributor.authorAkgun, Ozgur
dc.contributor.authorMiguel, Ian James
dc.contributor.authorNightingale, Peter William
dc.contributor.editorTong, Hanghang
dc.contributor.editorLi, Zhenhui (Jessie)
dc.contributor.editorZhu, Feida
dc.contributor.editorYu, Jeffrey
dc.date.accessioned2018-12-04T09:30:05Z
dc.date.available2018-12-04T09:30:05Z
dc.date.issued2018-11-17
dc.identifier.citationKocak , G , Akgun , O , Miguel , I J & Nightingale , P W 2018 , Closed frequent itemset mining with arbitrary side constraints . in H Tong , Z J Li , F Zhu & J Yu (eds) , 2018 IEEE International Conference on Data Mining Workshops (ICDMW) . , 8637581 , IEEE Computer Society , pp. 1224 - 1232 , Workshop on Optimization Based Techniques for Emerging Data Mining Problems (OEDM 2018) , Sentosa Island , Singapore , 17/11/18 . https://doi.org/10.1109/ICDMW.2018.00175en
dc.identifier.citationworkshopen
dc.identifier.isbn9781538692899
dc.identifier.isbn9781538692882
dc.identifier.otherPURE: 256767847
dc.identifier.otherPURE UUID: 921a03b3-7465-4acd-b3cf-8b608e1ef86a
dc.identifier.otherORCID: /0000-0002-5052-8634/work/51261018
dc.identifier.otherORCID: /0000-0001-9519-938X/work/51261071
dc.identifier.otherScopus: 85062871776
dc.identifier.otherWOS: 000465766800166
dc.identifier.otherORCID: /0000-0002-6317-0141/work/62668487
dc.identifier.urihttp://hdl.handle.net/10023/16617
dc.description.abstractFrequent itemset mining (FIM) is a method for finding regularities in transaction databases. It has several application areas, such as market basket analysis, genome analysis, and drug design. Finding frequent itemsets allows further analysis to focus on a small subset of the data. For large datasets the number of frequent itemsets can also be very large, defeating their purpose. Therefore, several extensions to FIM have been studied, such as adding high-utility (or low-cost) constraints and only finding closed (or maximal) frequent itemsets. This paper presents a constraint programming based approach that combines arbitrary side constraints with closed frequent itemset mining. Our approach allows arbitrary side constraints to be expressed in a high level and declarative language which is then translated automatically for efficient solution by a SAT solver. We compare our approach with state-of-the-art algorithms via the MiningZinc system (where possible) and show significant contributions in terms of performance and applicability.
dc.format.extent9
dc.language.isoeng
dc.publisherIEEE Computer Society
dc.relation.ispartof2018 IEEE International Conference on Data Mining Workshops (ICDMW)en
dc.rights© 2018, IEEE. This work has been made available online in accordance with the publisher's policies. This is the author created accepted version manuscript following peer review and as such may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1109/ICDMW.2018.00175en
dc.subjectData miningen
dc.subjectPattern miningen
dc.subjectFrequent itemset miningen
dc.subjectClosed frequent itemset miningen
dc.subjectConstraint modellingen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectDASen
dc.subject.lccQA75en
dc.titleClosed frequent itemset mining with arbitrary side constraintsen
dc.typeConference itemen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.contributor.institutionUniversity of St Andrews.Centre for Interdisciplinary Research in Computational Algebraen
dc.identifier.doihttps://doi.org/10.1109/ICDMW.2018.00175


This item appears in the following Collection(s)

Show simple item record