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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References
Feng, Y., You, H., Zhang, Z., Ji, R., Gao, Y.: Hypergraph neural networks. In: AAAI 2019, pp. 3558–3565 (2019)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: ACM SIGMOD 2000, pp. 1–12. ACM (2000)
Horváth, T., Bringmann, B., De Raedt, L.: Frequent hypergraph mining. In: Muggleton, S., Otero, R., Tamaddoni-Nezhad, A. (eds.) ILP 2006. LNCS (LNAI), vol. 4455, pp. 244–259. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73847-3_26
Inokuchi, A., Washio, T., Motoda, H.: An Apriori-based algorithm for mining frequent substructures from graph data. In: Zighed, D.A., Komorowski, J., Żytkow, J. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 13–23. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45372-5_2
Kuramochi, M., Karypis, G.: Frequent subgraph discovery. In: IEEE ICDM 2001, pp. 313–320. IEEE (2001)
Pei, J., et al.: PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth. In: ICDE 2001, pp. 215–224. IEEE (2001)
Rousseau, F., Kiagias, E., Vazirgiannis, M.: Text categorization as a graph classification problem. In: ACL-IJCNLP 2015, pp. 1702–1712 (2015)
Srikant, R., Agrawal, R.: Mining sequential patterns: generalizations and performance improvements. In: Apers, P., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 1–17. Springer, Heidelberg (1996). https://doi.org/10.1007/BFb0014140
Srikant, R., Vu, Q., Agrawal, R.: Mining association rules with item constraints. In: KDD 1997, pp. 67–73 (1997)
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: ArnetMiner: extraction and mining of academic social networks. In: KDD 2008, pp. 990–998 (2008)
Yadati, N., Nimishakavi, M., Yadav, P., Nitin, V., Louis, A., Talukdar, P.: HyperGCN: a new method for training graph convolutional networks on hypergraphs. In: NeurIPS 2019, pp. 1511–1522 (2019)
Yan, X., Han, J.: gSpan: Graph-based substructure pattern mining. In: IEEE ICDM 2002, pp. 721–724 (2002)
Zhou, D., Huang, J., Schölkopf, B.: Learning with hypergraphs: clustering, classification, and embedding. In: NIPS 2006, pp. 1601–1608 (2006)
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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