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An Efficient Algorithm to Mine High Average-Utility Sequential Patterns

  • Tiantian XuEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)

Abstract

Mining high-utility sequential patterns (HUSP) has attracted increasing attention in recent years. Current studies of HUSP mining, however, don’t consider the length of sequential patterns, which result in the problem of a huge amount of longer sequential patterns in the mining results. In order to obtain more decision-making information, this paper introduces an average utility measure and proposes HAUSPM method to efficiently mine high average-utility sequential patterns (HAUSP) by considering the length of sequential patterns. Extensive experiments show that the total number of HAUSP mined by HAUSPM is fewer than HUSP under the same minimum utility.

Keywords

High average-utility sequential pattern mining Sequence analysis Utility mining Data mining 

Notes

Acknowledgements

This work was supported by the Natural Science Foundation of Shandong Province, China (ZR2019BF018) and National Natural Science Foundation of China (61806105).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyQilu University of Technology (Shandong Academy of Sciences)JinanChina

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