Abstract
Hypergraph is a complex data structure capable of expressing associations among any number of data entities. Overcoming the limitations of traditional graphs, hypergraphs are useful to model real-life problems. Frequent pattern mining is one of the most popular problems in data mining with a lot of applications. To the best of our knowledge, there exists no flexible frequent pattern mining framework for hypergraph databases decomposing associations among data entities. In this work, we propose a flexible and complete framework for mining frequent patterns from a collection of hypergraphs. We also develop an algorithm for mining frequent subhypergraphs by introducing a canonical labeling technique for isomorphic subhypergraphs. Experiments conducted on real-life hypergraph databases demonstrate both the efficiency of the algorithm and the effectiveness of the proposed framework.
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Acknowledgement
This work is partially funded by (a) ICT Division, Government of People’s Republic of Bangladesh; (b) NSERC (Canada); and (c) University of Manitoba.
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Alam, M.T., Ahmed, C.F., Samiullah, M., Leung, C.K. (2021). Mining Frequent Patterns from Hypergraph Databases. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12713. Springer, Cham. https://doi.org/10.1007/978-3-030-75765-6_1
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DOI: https://doi.org/10.1007/978-3-030-75765-6_1
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