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Mining Regional High Utility Co-location Pattern

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Spatial Data and Intelligence (SpatialDI 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14619))

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Abstract

A co-location pattern is a set of spatial features whose instances are frequently located together in geo-space. In real world, different instances have different distributions and different values. However, existing methods for mining pattern ignore these differences. In this paper, we propose a novel method for mining regional high utility co-location pattern by considering both instance distribution and value. First, local regions are obtained based on fuzzy density peak clustering. Then, the regional high utility co-location pattern is defined, and an efficient algorithm for mining the patterns in local regions is presented by pruning unpromising patterns. The experiment results show the patterns are meaningful and the mining algorithm is efficient.

M. Xiong—Student author.

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References

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (62266050, 62276227), the Program for Young and Middle-aged Academic and Technical Reserve Leaders of Yunnan Province (202205AC160033), Yunnan Provincial Major Science and Technology Special Plan Projects (202202AD080003), Yunnan Fundamental Research Projects (202201AS070015).

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Authors and Affiliations

  1. School of Information Science and Engineering, Yunnan University, Kunming, China

    Meiyu Xiong, Hongmei Chen, Lizhen Wang & Qing Xiao

  2. Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, Kunming, China

    Hongmei Chen & Lizhen Wang

Authors
  1. Meiyu Xiong
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  2. Hongmei Chen
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  3. Lizhen Wang
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  4. Qing Xiao
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Corresponding author

Correspondence to Hongmei Chen .

Editor information

Editors and Affiliations

  1. Renmin University of China, Beijing, China

    Xiaofeng Meng

  2. Nanjing Normal University, Nanjing, China

    Xueying Zhang

  3. Beijing University of Chemical Technology, Beijing, China

    Danhuai Guo

  4. Nanjing Normal University, Nanjing, China

    Di Hu

  5. Huazhong University of Science and Technology, Wuhan, China

    Bolong Zheng

  6. Hefei University of Technology, Hefei, China

    Chunju Zhang

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Xiong, M., Chen, H., Wang, L., Xiao, Q. (2024). Mining Regional High Utility Co-location Pattern. In: Meng, X., Zhang, X., Guo, D., Hu, D., Zheng, B., Zhang, C. (eds) Spatial Data and Intelligence. SpatialDI 2024. Lecture Notes in Computer Science, vol 14619. Springer, Singapore. https://doi.org/10.1007/978-981-97-2966-1_8

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  • DOI: https://doi.org/10.1007/978-981-97-2966-1_8

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2965-4

  • Online ISBN: 978-981-97-2966-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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