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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.identifier.citationKocak , G , Akgun , O , Miguel , I J & Nightingale , P W 2018 , Closed frequent itemset mining with arbitrary side constraints . in H Tong , Z 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 .
dc.identifier.otherORCID: /0000-0002-5052-8634/work/51261018
dc.identifier.otherORCID: /0000-0001-9519-938X/work/51261071
dc.identifier.otherORCID: /0000-0002-6317-0141/work/62668487
dc.identifier.otherORCID: /0000-0002-6930-2686/work/68281450
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.publisherIEEE Computer Society
dc.relation.ispartof2018 IEEE International Conference on Data Mining Workshops (ICDMW)en
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.titleClosed frequent itemset mining with arbitrary side constraintsen
dc.typeConference itemen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.contributor.institutionUniversity of St Andrews. Centre for Interdisciplinary Research in Computational Algebraen

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