Gökberk Koçak (one of our PhD students) presented our paper at OEDM’18 (The Workshop on Optimization Based Techniques for Emerging Data Mining Problems), which was co-located with IEEE ICDM 2018 (IEEE International Conference on Data Mining) in Singapore.

The paper can be found on the IEEE website.

Paper title: Closed frequent itemset mining with arbitrary side constraints

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.