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UMine: Study on Prevalent Co-locations Mining from Uncertain Data Sets

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 849)

Abstract

We can collect a large amount of spatial data by utilizing sensor positioning technology and wearable devices. However, most of the acquired data are uncertain because of the gaps in data collection or to maintain subject privacy. Thus, we investigate co-location pattern mining problem in the context of uncertain data. The prevalent co-location pattern under uncertain environments has two different definitions. The first definition, referred as the expected prevalent co-location, employs the expected interest degree of co-location to measure whether this pattern is frequent. The second definition, referred as the probabilistic prevalent co-location, uses the probabilistic formulations to measure frequency. Here a novel system called UMine is proposed to compare this two different definitions with a user-friendly interface. The core of a system such as this is the mining algorithm, and UMine is integrated with the expected mining method, probabilistic mining method, and approximate mining method. In this paper, the system is introduced in detail, and the comparison between these two types of definitions is implemented. The experimental results show that the difference between these two definitions’ result sets changes as the threshold changes. By flexibly adjusting the parameters, users can observe interesting patterns in the data. In addition, the demonstration provides data generation and preprocessing function while showing its practicality for either real-world or synthetic data sets. The study can also provide support for the further uncertain Co-location patterns mining research.

Keywords

Spatial co-location patterns Uncertain data Possible worlds Visualization 

Notes

Acknowledgment

This paper was supported by the Research Foundation of Educational Department of Yunnan Province (No. 2016ZZX304).

References

  1. 1.
    Morimoto, Y.: Mining frequent neighboring class sets in spatial databases. In: Proceedings of SIGKDD, pp. 353–358 (2001)Google Scholar
  2. 2.
    Huang, Y., Shekhar, S., Xiong, H.: Discovering colocation patterns from spatial data sets: a general approach. IEEE Trans. Knowl. Data Eng. (TKDE) 16(12), 1472–1485 (2004)CrossRefGoogle Scholar
  3. 3.
    Yoo, J.S., Shekhar, S.: A Partial Join approach for mining co-location patterns. In: Proceedings of the 12th Annual ACM International Workshop on Geographic Information Systems, pp. 241–249. ACM Press (2004)Google Scholar
  4. 4.
    Yoo, J.S., Shekhar, S., Celik, M.: A Join-Less approach for co-location pattern mining: a summary of result. In: Proceedings of the 5th IEEE International Conference on Data Mining (ICDM), pp. 813–816. IEEE Press (2005)Google Scholar
  5. 5.
    Wang, L., Zhou, L., Lu, J., Yip, J.: An order-clique-based approach for mining maximal Co-locations. Inf. Sci. 179(19), 3370–3382 (2009)CrossRefGoogle Scholar
  6. 6.
    Deng, M., Cai, J., Liu, Q., et al.: Multi-level method for discovery of regional co-location patterns. Int. J. Geograph. Inf. Sci. 31, 1–25 (2017)CrossRefGoogle Scholar
  7. 7.
    Jiang, Y., Wang, L., Lu, Y., et al.: Discovering both positive and negative co-location rules from spatial data sets. In: International Conference on Software Engineering and Data Mining, pp. 398–403. IEEE (2010)Google Scholar
  8. 8.
    Yue, H., Zhu, X., Ye, X., et al.: The local colocation patterns of crime and land-use features in Wuhan, China. Int. J. Geo-Information 6(10), 307 (2017)CrossRefGoogle Scholar
  9. 9.
    Wang, L., Chen, H., Zhao, L., Zhou, L.: Efficiently mining co-location rules on interval data. In: Cao, L., Feng, Y., Zhong, J. (eds.) ADMA 2010. LNCS (LNAI), vol. 6440, pp. 477–488. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-17316-5_45CrossRefGoogle Scholar
  10. 10.
    Ye, L.U., Wang, L., et al.: Spatial co-location patterns mining over uncertain data based on possible worlds. J. Comput. Res. Dev. 47(Supp l.), 215–221 (2010). (in Chinese with English abstract)Google Scholar
  11. 11.
    Wang, L., Wu, P., Chen, H.: Finding probabilistic prevalent colocations in spatially uncertain data sets. TKDE 25(4), 790–804 (2013)Google Scholar
  12. 12.
    Liu, B., Chen, L., Liu, C., Zhang, C., Qiu, W.: Mining co-locations from continuously distributed uncertain spatial data. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds.) APWeb 2016. LNCS, vol. 9931, pp. 66–78. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-45814-4_6CrossRefGoogle Scholar
  13. 13.
    Wang, L., Lu, Y., Chen, H., Xiao, Q.: Prefix-tree-based spatial co-location patterns mining algorithms. J. Comput. Res. Dev. 47(Suppl.), 370–377 (2010). (in Chinese with English abstract)Google Scholar
  14. 14.
    Wang, L., Bao, Y., Lu, J., et al.: A web-based visual spatial co-location patterns’ mining prototype system (SCPMiner). In: International Conference on Cyberworlds, pp. 675–681. IEEE (2009)Google Scholar
  15. 15.
    Green, T.J., Tannen, V.: Models for incomplete and probabilistic information. In: IEEE Data Engineering Bulletin, pp. 278–296 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Pingping Wu
    • 1
  • Lizhen Wang
    • 2
  • Wenjing Yang
    • 1
  • Zhulin Su
    • 1
  1. 1.Dianchi College of Yunnan UniversityKunmingChina
  2. 2.School of Information Science and EngineeringYunnan UniversityKunmingChina

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