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UPFP-growth++: An Efficient Algorithm to Find Periodic-Frequent Patterns in Uncertain Temporal Databases.

, , , , , и . ICONIP (5), том 1792 из Communications in Computer and Information Science, стр. 182-194. Springer, (2022)

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Koji Kameo

Koji Onoda

Koji Ota

Tomoko Kojima

Hajime Kojima

 

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Foreword.. IEICE Trans. Inf. Syst., 96-D (5): 1005 (2013)Parallel Mining of Partial Periodic Itemsets in Big Data., , , , , , и . IEA/AIE, том 12144 из Lecture Notes in Computer Science, стр. 807-819. Springer, (2020)Dynamically pre-trained deep recurrent neural networks using environmental monitoring data for predicting PM2.5., , и . Neural Comput. Appl., 27 (6): 1553-1566 (2016)Human Factors in Cyber-Physical Social Systems: Leveraging Social Sensor Data., и . EJC, том 280 из Frontiers in Artificial Intelligence and Applications, стр. 157-165. IOS Press, (2015)A relation-based services management mechanism for service computing., , , и . IUCS, стр. 32-39. IEEE, (2010)SOBEX: distributed service Search engine that exploits service cOllaBoration contEXt., , , , и . IUCS, том 398 из ACM International Conference Proceeding Series, стр. 261-268. ACM, (2009)Discovering aspect-based correlation of Web contents for cross-media information retrieval., , и . ICME, стр. 1015-1018. IEEE Computer Society, (2004)Mining Periodic-Frequent Patterns in Irregular Dense Temporal Databases Using Set Complements., , , , , , и . IEEE Access, (2023)Provenance-Based Security Risk Assessment Framework., и . J. Inf. Process., 22 (4): 617-625 (2014)HDSHUI-miner: a novel algorithm for discovering spatial high-utility itemsets in high-dimensional spatiotemporal databases., , , , , , и . Appl. Intell., 53 (8): 8536-8561 (апреля 2023)