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Mining \(\epsilon \)-Closed High Utility Co-location Patterns from Spatial Data

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Advanced Data Mining and Applications (ADMA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13725))

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Abstract

Traditional spatial prevalent co-location pattern mining is discovering groups of spatial features whose instances frequently appear together in nearby areas. However, it is unsuitable for many real-world applications where the significance of these instances must be considered. High utility co-location pattern (HUCP) mining is developed to find highly beneficial patterns by considering the importance of spatial instances. However, the mining result typically contains many HUCPs, making it difficult for users to absorb, comprehend, and apply. This work proposes a compressed representation of HUCPs, \(\epsilon \)-closed HUCPs, that allow for a user-specified small tolerance of the information between a pattern and its supersets. If the information difference is not larger than the small tolerance it only needs to keep the supersets. Moreover, an efficient algorithm is developed to discover \(\epsilon \)-closed HUCPs. The proposed algorithm avoids examining many unnecessary candidates; therefore, the performance of mining \(\epsilon \)-closed HUCPs is significantly improved. A set of different numbers of features, numbers of instances, and distribution of both synthetic and real data sets are employed to evaluate the performance of the proposed method completely. The experimental results show that \(\epsilon \)-closed balances the compression rate and the UPI error rate and gives a large pattern compression rate within a relatively small range of error rates. Moreover, the proposed algorithm is high-performance on dense and large data sets.

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References

  1. Akbari, M., Samadzadegan, F., Weibel, R.: A generic regional spatio-temporal co-occurrence pattern mining model: a case study for air pollution. Journal of Geographical Systems 17(3), 249–274 (2015)

    Google Scholar 

  2. Andrzejewski, W., Boinski, P.: Parallel approach to incremental co-location pattern mining. Inf. Sci. 496, 485–505 (2019)

    Article  Google Scholar 

  3. Bao, X., Wang, L.: A clique-based approach for co-location pattern mining. Inf. Sci. 490, 244–264 (2019)

    Article  Google Scholar 

  4. Cai, J., Liu, Q., Deng, M., Tang, J., He, Z.: Adaptive detection of statistically significant regional spatial co-location patterns. Comput. Environ. Urban Syst. 68, 53–63 (2018)

    Article  Google Scholar 

  5. Chang, L.: Efficient maximum clique computation over large sparse graphs. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 529–538 (2019)

    Google Scholar 

  6. Eppstein, D., Löffler, M., Strash, D.: Listing all maximal cliques in large sparse real-world graphs in near-optimal time. J. Exp. Algorithmics (JEA) 18, 1–3 (2013)

    MATH  Google Scholar 

  7. He, Z., Deng, M., Xie, Z., Wu, L., Chen, Z., Pei, T.: Discovering the joint influence of urban facilities on crime occurrence using spatial co-location pattern mining. Cities 99, 102612 (2020)

    Article  Google Scholar 

  8. 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 

  9. Lee, I., Phillips, P.: Urban crime analysis through areal categorized multivariate associations mining. Appl. Artif. Intell. 22(5), 483–499 (2008)

    Article  Google Scholar 

  10. Leibovici, D.G., Claramunt, C., Le Guyader, D., Brosset, D.: Local and global spatio-temporal entropy indices based on distance-ratios and co-occurrences distributions. Int. J. Geogr. Inf. Sci. 28(5), 1061–1084 (2014)

    Article  Google Scholar 

  11. Li, J., Adilmagambetov, A., Mohomed Jabbar, M.S., Zaïane, O.R., Osornio-Vargas, A., Wine, O.: On discovering co-location patterns in datasets: a case study of pollutants and child cancers. GeoInformatica 20(4), 651–692 (2016)

    Google Scholar 

  12. Liu, Q., Liu, W., Deng, M., Cai, J., Liu, Y.: An adaptive detection of multilevel co-location patterns based on natural neighborhoods. Int. J. Geogr. Inf. Sci. 35(3), 556–581 (2021)

    Article  Google Scholar 

  13. Luna, J.M., Fournier-Viger, P., Ventura, S.: Frequent itemset mining: a 25 years review. Wires Data. Min. Knowl. 9(6), e1329 (2019)

    Article  Google Scholar 

  14. Raj, S., Ramesh, D., Sreenu, M., Sethi, K.K.: Eafim: efficient apriori-based frequent itemset mining algorithm on spark for big transactional data. Knowl. Inf. Syst. 62(9), 3565–3583 (2020)

    Article  Google Scholar 

  15. Sheshikala, M., Rao, D.R., Prakash, R.V.: A map-reduce framework for finding clusters of colocation patterns-a summary of results. In: 2017 IEEE 7th International Advance Computing Conference (IACC), pp. 129–131. IEEE (2017)

    Google Scholar 

  16. Tran, V., Wang, L., Chen, H., Xiao, Q.: MCHT: a maximal clique and hash table-based maximal prevalent co-location pattern mining algorithm. Expert Syst. Appl. 175, 114830 (2021)

    Article  Google Scholar 

  17. Tran, V., Wang, L., Zhou, L.: A spatial co-location pattern mining framework insensitive to prevalence thresholds based on overlapping cliques. Distributed Parallel Databases 1–38 (2021)

    Google Scholar 

  18. Wang, L., Bao, X., Chen, H., Cao, L.: Effective lossless condensed representation and discovery of spatial co-location patterns. Inform. Sci. 436, 197–213 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  19. 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.B., Chang, L., Hua, W. (eds.) DASFAA 2017. LNCS, vol. 10178, pp. 458–474. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55699-4_28

    Chapter  Google Scholar 

  20. Yao, X., Jiang, X., Wang, D., Yang, L., Peng, L., Chi, T.: Efficiently mining maximal co-locations in a spatial continuous field under directed road networks. Inf. Sci. 542, 357–379 (2021)

    Article  MathSciNet  Google Scholar 

  21. Yoo, J.S., Bow, M.: A framework for generating condensed co-location sets from spatial databases. Intell. Data Anal. 23(2), 333–355 (2019)

    Article  Google Scholar 

  22. Yoo, J.S., Shekhar, S.: A joinless approach for mining spatial colocation patterns. IEEE Trans. Knowl. Data Eng. 18(10), 1323–1337 (2006)

    Article  Google Scholar 

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (61966036), the Project of Innovative Research Team of Yunnan Province (2018HC019), the Yunnan Fundamental Research Projects (202201AS070015).

Author information

Authors and Affiliations

  1. FPT University, Hanoi, 155514, Vietnam

    Vanha Tran & SonTung Pham

  2. Yunnan University, Kunming, 650091, China

    Lizhen Wang, Shiyu Zhang & Jinpeng Zhang

  3. Dianchi College of Yunnan University, Kunming, 650213, China

    Lizhen Wang

  4. Yunnan University of Finance and Economics, Kunming, 650221, China

    Jinpeng Zhang

Authors
  1. Vanha Tran
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  2. Lizhen Wang
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  3. Shiyu Zhang
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  4. Jinpeng Zhang
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  5. SonTung Pham
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Corresponding author

Correspondence to Lizhen Wang .

Editor information

Editors and Affiliations

  1. The University of Adelaide, Adelaide, SA, Australia

    Weitong Chen

  2. The University of New South Wales, Sydney, NSW, Australia

    Lina Yao

  3. Macquarie University, Sydney, NSW, Australia

    Taotao Cai

  4. Griffith University, Brisbane, QLD, Australia

    Shirui Pan

  5. Microsoft, Beijing, China

    Tao Shen

  6. The University of Queensland, Brisbane, QLD, Australia

    Xue Li

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Tran, V., Wang, L., Zhang, S., Zhang, J., Pham, S. (2022). Mining \(\epsilon \)-Closed High Utility Co-location Patterns from Spatial Data. In: Chen, W., Yao, L., Cai, T., Pan, S., Shen, T., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13725. Springer, Cham. https://doi.org/10.1007/978-3-031-22064-7_30

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  • DOI: https://doi.org/10.1007/978-3-031-22064-7_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22063-0

  • Online ISBN: 978-3-031-22064-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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