Closed frequent itemset mining with arbitrary side constraints
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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.
Kocak , 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 . DOI: 10.1109/ICDMW.2018.00175workshop
2018 IEEE International Conference on Data Mining Workshops (ICDMW)
© 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.2018.00175
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