Kocak, Gokberk, Akgun, Ozgur, Miguel, Ian James et al. (1 more author) (2018) Closed frequent itemset mining with arbitrary side constraints. In: Workshop proceedings (OEDM 2018) of the 2018 IEEE International Conference on Data Mining (ICDM). IEEE Computer Society
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 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. |
Keywords: | Data mining,Pattern mining,Closed frequent itemset mining,Constraint modelling,Frequent itemset mining |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 23 Jan 2019 09:30 |
Last Modified: | 26 Jan 2025 00:05 |
Status: | Published |
Publisher: | IEEE Computer Society |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:141510 |