Closed frequent itemset mining with arbitrary side constraints

Gokberk Kocak, Ozgur Akgun, Ian James Miguel, Peter William Nightingale

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)
2 Downloads (Pure)


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.
Original languageEnglish
Title of host publication2018 IEEE International Conference on Data Mining Workshops (ICDMW)
EditorsHanghang Tong, Zhenhui (Jessie) Li, Feida Zhu, Jeffrey Yu
PublisherIEEE Computer Society
Pages1224 - 1232
Number of pages9
ISBN (Electronic)9781538692882
ISBN (Print)9781538692899
Publication statusPublished - 17 Nov 2018
EventWorkshop on Optimization Based Techniques for Emerging Data Mining Problems (OEDM 2018) - Sentosa Island, Singapore
Duration: 17 Nov 201820 Nov 2018


WorkshopWorkshop on Optimization Based Techniques for Emerging Data Mining Problems (OEDM 2018)
Abbreviated titleOEDM 2018
CitySentosa Island
Internet address


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


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