Efficient chain structure for high-utility sequential pattern mining

Lin, Jerry Chun-Wei and Li, Yuanfa and Fournier-Viger, Philippe and Djenouri, Youcef and Zhang, Ji (2020) Efficient chain structure for high-utility sequential pattern mining. IEEE Access, 8:9016187. pp. 40714-40722.

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High-utility sequential pattern mining (HUSPM) is an emerging topic in data mining, which considers both utility and sequence factors to derive the set of high-utility sequential patterns (HUSPs) from the quantitative databases. Several works have been presented to reduce the computational cost by variants of pruning strategies. In this paper, we present an efficient sequence-utility (SU)-chain structure, which can be used to store more relevant information to improve mining performance. Based on the SU-Chain structure, the existing pruning strategies can also be utilized here to early prune the unpromising candidates and obtain the satisfied HUSPs. Experiments are then compared with the state-of-the-art HUSPM algorithms and the results showed that the SU-Chain-based model can efficiently improve the efficiency performance than the existing HUSPM algorithms in terms of runtime and number of the determined candidates.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Faculty/School / Institute/Centre: Historic - Institute for Resilient Regions - Centre for Health, Informatics and Economic Research (1 Aug 2018 - 31 Mar 2020)
Date Deposited: 23 Apr 2020 02:02
Last Modified: 08 May 2020 02:46
Uncontrolled Keywords: high utility sequential pattern mining, sequence, SU-Chain structure, data mining
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4699 Other information and computing sciences > 469999 Other information and computing sciences not elsewhere classified
Identification Number or DOI: https://doi.org/10.1109/ACCESS.2020.2976662
URI: http://eprints.usq.edu.au/id/eprint/38436

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