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International Conference on Big Data Analytics and Knowledge Discovery

DaWaK 2016: Big Data Analytics and Knowledge Discovery pp 3–18Cite as

Mining Recent High-Utility Patterns from Temporal Databases with Time-Sensitive Constraint

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9829))

Abstract

Useful knowledge embedded in a database is likely to be changed over time. Identifying recent changes and up-to-date information in temporal databases can provide valuable information. In this paper, we address this issue by introducing a novel framework, named recent high-utility pattern mining from temporal databases with time-sensitive constraint (RHUPM) to mine the desired patterns based on user-specified minimum recency and minimum utility thresholds. An efficient tree-based algorithm called RUP, the global and conditional downward closure (GDC and CDC) properties in the recency-utility (RU)-tree are proposed. Moreover, the vertical compact recency-utility (RU)-list structure is adopted to store necessary information for later mining process. The developed RUP algorithm can recursively discover recent HUPs; the computational cost and memory usage can be greatly reduced without candidate generation. Several pruning strategies are also designed to speed up the computation and reduce the search space for mining the required information.

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Acknowledgment

This research was partially supported by the Tencent Project under grant CCF-TencentRAGR20140114, and by the National Natural Science Foundation of China under grant No.61503092.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China

    Wensheng Gan, Jerry Chun-Wei Lin & Han-Chieh Chao

  2. School of Natural Sciences and Humanities, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China

    Philippe Fournier-Viger

  3. Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan

    Han-Chieh Chao

Authors
  1. Wensheng Gan
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  2. Jerry Chun-Wei Lin
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  3. Philippe Fournier-Viger
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  4. Han-Chieh Chao
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Corresponding author

Correspondence to Jerry Chun-Wei Lin .

Editor information

Editors and Affiliations

  1. University of Science and Technology , Rolla, Missouri, USA

    Sanjay Madria

  2. Osaka University , Osaka, Japan

    Takahiro Hara

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© 2016 Springer International Publishing Switzerland

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Gan, W., Lin, J.CW., Fournier-Viger, P., Chao, HC. (2016). Mining Recent High-Utility Patterns from Temporal Databases with Time-Sensitive Constraint. In: Madria, S., Hara, T. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2016. Lecture Notes in Computer Science(), vol 9829. Springer, Cham. https://doi.org/10.1007/978-3-319-43946-4_1

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  • DOI: https://doi.org/10.1007/978-3-319-43946-4_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-43945-7

  • Online ISBN: 978-3-319-43946-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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