By the same authors

Closed frequent itemset mining with arbitrary side constraints

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Title of host publicationWorkshop proceedings (OEDM 2018) of the 2018 IEEE International Conference on Data Mining (ICDM)
DateAccepted/In press - 12 Sep 2018
DatePublished (current) - 17 Nov 2018
Number of pages9
PublisherIEEE Computer Society
Original languageEnglish

Abstract

Frequent 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.

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© IEEE, 2019. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.

    Research areas

  • Data mining, Pattern mining, Closed frequent itemset mining, Constraint modelling, Frequent itemset mining

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