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An efficient algorithm for mining closed high utility itemsets over data streams with one dataset scan

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Abstract

The high utility itemsets mining over data streams will produce many redundant itemsets. To remove redundant itemsets, the researchers proposed to mine the closed high utility itemsets, the number of which is much smaller than that of the complete high utility itemsets and the result is lossless. However, the existing closed high utility itemsets mining algorithm over data streams needs to scan the dataset twice, and this algorithm that requires multiple scans cannot meet the real-time processing requirements of the streaming environment. To solve the above problem, this paper proposed a new algorithm CHUIDS_OSc that only needs to scan the original dataset once to achieve mining closed high utility itemsets over data streams. A new utility-list structure is designed in CHUIDS_OSc, and this structure can quickly complete the construction and update of batch information without rescanning the original dataset. In addition, effective pruning strategies are applied to improve the closed itemsets mining process and eliminate potential low utility candidates. Experimental evaluations show the efficiency and feasibility of the algorithm for scanning and processing datasets. As far as the running time is concerned, it is better than the previously proposed closed high utility itemsets mining algorithms that require multiple scans over data streams.

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Acknowledgements

This work was supported by the National Nature Science Foundation of China (62062004), the Ningxia Natural Science Foundation Project (2020AAC03216), and the North Minzu University Innovation Project Fund (YCX20077).

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  1. School of Computer Science and Engineering, North Minzu University, Xixia District, Yinchuan, 750021, Ningxia Hui Autonomous Region, China

    Meng Han, Haodong Cheng, Ni Zhang, Xiaojuan Li & Le Wang

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Han, M., Cheng, H., Zhang, N. et al. An efficient algorithm for mining closed high utility itemsets over data streams with one dataset scan. Knowl Inf Syst 65, 207–240 (2023). https://doi.org/10.1007/s10115-022-01763-9

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