Skip to main content
Springer Nature Link
Log in

Mining Regional High Utility Co-location Pattern

  • Conference paper
  • First Online:
Spatial Data and Intelligence (SpatialDI 2024)

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

Included in the following conference series:

  • 352 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Wang, L., Bao, X., Zhou, L., et al.: Maximal sub-prevalent co-location patterns and efficient mining algorithms. In: Bouguettaya, A., et al. (eds.) Web Information Systems Engineering – WISE 2017. WISE 2017. LNCS, vol. 10569, pp. 199–214. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68783-4_14

  2. Wang, L., Bao, X., Zhou, L., et al.: Mining maximal co-location patterns. World Wide Web 22(5), 1971–1997 (2019)

    Google Scholar 

  3. Yang, S., Wang, L., Bao, X., Lu, J.: A framework for mining spatial high utility co-location patterns. In: FSKD 2015, Proceedings of the 12th International Conference on Fuzzy Systems and Knowledge Discovery, pp. 595–601. IEEE, Zhangjiajie, China (2015)

    Google Scholar 

  4. Jiang, X., Wang, L., Tran, V.: A parallel algorithm for regional co-location mining based on fuzzy density peak clustering. Sin Sci. Inform. 53(7), 1281–1298 (2023)

    Google Scholar 

  5. Huang, Y., Shekhar, S., Xiong, H.: Discovering colocation patterns from spatial data sets: a general approach. IEEE Trans. Knowl. Data Eng. 16(12), 1472–1485 (2004)

    Article  Google Scholar 

  6. Wang, L., Jiang, W., Chen, H., Fang, Y.: Efficiently mining high utility co-location patterns from spatial data sets with instance-specific utilities. In: Candan, S., Chen, L., Pedersen, T., Chang, L., Hua, W. (eds.) Database Systems for Advanced Applications. DASFAA 2017. LNCS, vol. 10178, pp. 458–474. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55699-4_28

  7. Xiao-Xuan, W., Li-Zhen, W., Hong-Mei, C., et al.: Mining spatial high utility co-location patterns based on feature utility ratio. Chin. J. Comput. 42(8), 1721–1738 (2019)

    Google Scholar 

  8. Luo, J., Wang, L.-Z., Wang, X.-X., Xiao, Q.: Mining spatial high utility core patterns under k-nearest neighbors. Chin. J. Comput. 45(2), 354–368 (2022)

    Google Scholar 

  9. Qian, F., Chiew, K., He, Q.M., et al.: Mining regional co-location patterns with kNNG. J. Intell. Inf. Syst. 42, 485–505 (2014)

    Article  Google Scholar 

  10. Yu, W.H.: Identifying and analyzing the prevalent regions of a co-Location pattern using polygons clustering approach. ISPRS Int. J. Geo-Inf. 6(9), 259 (2017)

    Google Scholar 

Download references

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).

Author information

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongmei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lizhen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qing Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Rights and permissions

Reprints and permissions

Copyright information

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

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2965-4

  • Online ISBN: 978-981-97-2966-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics