Abstract
One of the most active research topics in data mining is pattern discovery involving the well-known task of enumerating interesting patterns from databases. The problem of mining high utility itemsets is to find the set of items with the highest utility values based on a given minimum utility threshold. However, due to the advancement of big data technologies, finding all itemsets is much more harder due to the huge number of patterns and the large required resources. Parallel processing is an effective way to efficiently address the problem of mining patterns from large databases. Based on classical propositional logic, we propose in this paper a parallel method to handle efficiently the problem of discovering high utility itemsets from transaction databases. To do this, a decomposition technique is used to splitting the original problem of mining high utility itemsets into smaller and independent sub-problems that can be handled easily in a parallel manner. Then, empirical evaluations on different real-world datasets show that the proposed method is very efficient while being flexible enough to handle additional user constraints when discovering closed high utility itemsets.
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References
Atmaja, E.H.S., Sonawane, K.: Parallel algorithm to efficiently mine high utility itemset. In: ICT Analysis and Applications, pp. 167–178 (2022)
Böhm, M., Speckenmeyer, E.: A fast parallel sat-solver-efficient workload balancing. Ann. Math. Artif. Intell. 17, 381–400 (1996). https://doi.org/10.1007/BF02127976
Coquery, E., Jabbour, S., Sais, L., Salhi, Y., et al.: A sat-based approach for discovering frequent, closed and maximal patterns in a sequence. In: ECAI, pp. 258–263 (2012)
Duong, Q.-H., Fournier-Viger, P., Ramampiaro, H., Nørvåg, K., Dam, T.-L.: Efficient high utility itemset mining using buffered utility-lists. Appl. Intell. 48(7), 1859–1877 (2017). https://doi.org/10.1007/s10489-017-1057-2
Eén, N., Sörensson, N.: An extensible SAT-solver. In: Giunchiglia, E., Tacchella, A. (eds.) SAT 2003. LNCS, vol. 2919, pp. 502–518. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24605-3_37
Fournier-Viger, P., Gomariz, A., Gueniche, T., Soltani, A., Wu, C.W., Tseng, V.S., et al.: SPMF: a java open-source pattern mining library. J. Mach. Learn. Res. 15, 3389–3393 (2014)
Hamadi, Y., Jabbour, S., Sais, L.: ManySAT: a parallel sat solver. J. Satisfiability Boolean Model. Comput. 6, 245–262 (2010)
Hidouri, A., Jabbour, S., Raddaoui, B., Yaghlane, B.B.: A sat-based approach for mining high utility itemsets from transaction databases. In: International Conference on Big Data Analytics and Knowledge Discovery, pp. 91–106 (2020)
Hidouri, A., Jabbour, S., Raddaoui, B., Yaghlane, B.B.: Mining closed high utility itemsets based on propositional satisfiability. Data Knowl. Eng. 136, 101927 (2021)
Jabbour, S., Mhadhbi, N., Raddaoui, B., Sais, L.: Sat-based models for overlapping community detection in networks. Computing 102(5), 1275–1299 (2020)
Jabbour, S., Mhadhbi, N., Raddaoui, B., Sais, L.: A declarative framework for maximal k-plex enumeration problems. In: AAMAS (2022, to appear)
Jabbour, S., Sais, L., Salhi, Y.: Mining top-k motifs with a sat-based framework. Artif. Intell. 244, 30–47 (2017)
Lin, Y.C., Wu, C.W., Tseng, V.S.: Mining high utility itemsets in big data. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 649–661 (2015)
Nguyen, T.D., Nguyen, L.T., Vo, B.: A parallel algorithm for mining high utility itemsets. In: International Conference on Information Systems Architecture and Technology, pp. 286–295 (2018)
Sethi, K.K., Ramesh, D., Edla, D.R.: P-FHM+: parallel high utility itemset mining algorithm for big data processing. Procedia Comput. Sci. 132, 918–927 (2018)
Tseitin, G.S.: On the complexity of derivation in propositional calculus. In: Automation of Reasoning, pp. 466–483 (1983)
Vo, B., Nguyen, L.T., Nguyen, T.D., Fournier-Viger, P., Yun, U.: A multi-core approach to efficiently mining high-utility itemsets in dynamic profit databases. IEEE Access 8, 85890–85899 (2020)
Zhang, H., Bonacina, M.P., Hsiang, J.: PSATO: a distributed propositional prover and its application to quasigroup problems. J. Symb. Comput. 21, 543–560 (1996)
Acknowledgements
This research has received support from the ANR CROQUIS (Collecting, Representing, cOmpleting, merging, and Querying heterogeneous and UncertaIn waStewater and stormwater network data) project, grant ANR-21-CE23-0004 of the French research funding agency Agence Nationale de la Recherche (ANR).
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Hidouri, A., Jabbour, S., Raddaoui, B., Chebbah, M., Ben Yaghlane, B. (2022). A Parallel Declarative Framework for Mining High Utility Itemsets. In: Ciucci, D., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2022. Communications in Computer and Information Science, vol 1602. Springer, Cham. https://doi.org/10.1007/978-3-031-08974-9_50
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DOI: https://doi.org/10.1007/978-3-031-08974-9_50
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