An efficient chain structure to mine high-utility sequential patterns

Lin, Jerry Chun-Wei and Li, Yuanfa and Fournier-Viger, Philippe and Djenouri, Youcef and Zhang, Ji (2019) An efficient chain structure to mine high-utility sequential patterns. In: 19th IEEE International Conference on Data Mining Workshops (ICDMW 2019), 8-11 Nov 2019, Beijing, China.


Abstract

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 from the quantitative databases. Several works have been presented to speed up 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 USpan, HUS-Span and HUSP-ULL approaches and the results of the developed structure can achieve good mining performance than the existing algorithms.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sept 2019 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sept 2019 -)
Date Deposited: 30 Jun 2020 06:00
Last Modified: 30 Jun 2020 06:00
Uncontrolled Keywords: utility mining, sequential pattern, SU-Chain, HUSPM
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Identification Number or DOI: 10.1109/ICDMW.2019.00146
URI: http://eprints.usq.edu.au/id/eprint/38120

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