Skip to main content
SpringerLink
Log in

Fast fuzzy trajectory clustering strategy based on data summarization and rough approximation

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

More technologies available for collecting large dataset of trajectory of moving objects make it more essential to perform clustering over these trajectory data. Trajectory clustering presents more complication than traditional approaches due to the nature of trajectories which is temporal, massive and related to the location. Meanwhile, the uncertainty in trajectory clustering also appears when we determine which cluster each trajectory belongs to. However, the computing performance of many clustering algorithms sharply declines as data size increases. In this paper, we study the fuzzy clustering approach for extracting potential spatial patterns by introducing rough set and fuzzy set theory. First, we propose the fast similarity measure method by employing the rough approximation distances between trajectories. Especially for the long trajectory sequences, the computing time would be reduced greatly. We also introduce a summarization technique to reduce the number of distance computations required in similarity measure. Second, an appropriate function of the membership degree is redefined for clustering quality and performance purpose. Third, we modify the fuzzy C-means algorithm by embodying a new similarity measure and the membership degree function. The experimental results conducted on two real datasets of trajectories show the effectiveness of our methods by evaluating clustering validity and computing performance for large datasets. The computing performance of the proposed fuzzy clustering is obviously improved as the dataset size of trajectories increases.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Elnekave, S., Last, M., Maimon, O.: Measuring similarity between trajectories of mobile objects. Stud. Comput. Intell. 91, 101–128 (2008)

    Google Scholar 

  2. Mao, F., Ji, M.H., Liu, T.: Mining spatiotemporal patterns of urban dwellers from taxi trajectory data. Front. Earth Sci. 10(2), 205–221 (2016)

    Article  Google Scholar 

  3. Chen, M., Liu, Y., Yu X. H.: Predicting next locations with object clustering and trajectory clustering. In: PAKDD 2015, Lecture Notes in Computer Science, vol. 9078, pp. 344–356 (2015)

  4. Tanzmeister, G., Wollherr, D., Buss, M.: Environment-based trajectory clustering to extract principal directions for autonomous vehicles. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 667–673. Chicago (2014)

  5. Chen, Z., Yan, Y., Ellis, T.: Lane detection by trajectory clustering in urban environments. In: 17th International Conference on Intelligent Transportation Systems, pp. 3076–3081. Qingdao (2014)

  6. Lin, Y.-C., Chang, T.-J., Lu, M.-M., Yu, H.-L.: A space-time typhoon trajectories analysis in the vicinity of Taiwan. Stoch. Environ. Res. Risk Assess. 29(7), 1857–1866 (2015)

    Article  Google Scholar 

  7. Li, Q., Li, D.: Big data GIS. Geomat. Inf. Sci. Wuhan Univ. 39(6), 641–644 (2014)

    Google Scholar 

  8. Lu, F., Zhang, H.: Big data and generalized GIS. Geomat. Inf. Sci. Wuhan Univ. 39(6), 645–654 (2014)

    Google Scholar 

  9. Gong, X., Pei, T., Sun, J., Luo, M.: Review of the Research Progresses in Trajectory Clustering Methods. Progr. Geogr. 30(5), 522–534 (2011)

    Google Scholar 

  10. Li, Y., Han, J., Yang, J.: Clustering moving objects. In: Proceedings of the 10th ACM SIGKDD International conference on Knowledge discovery and data mining, New York, pp.617–622 (2004)

  11. Hwang, S.Y., Liu, Y.H., Chiu, J.K., Lim, F.P.: Mining mobile group patterns: a trajectory-based approach. In: Proceedings of the PAKDD 2005, Hanoi, pp. 713–718(2005)

  12. Zhang, P., Deng, M., Van de Weghe, N.: Clustering spatio-temporal trajectories based on kernel density estimation. In: Lecture Notes in Computer Science, vol. 8579, pp. 298–311 (2014)

  13. Bernet, D.J., Clifford, J.: Finding Patterns in time series: a dynamic programming approach. In: Advances in Knowledge Discovery and Data Mining, American Association for Artificial Intelligence, pp. 229–248. Menlo park (1996)

  14. Ra, M., Lim, C., Song, Y., Jung, J., Kim, W.-Y.: Effective trajectory similarity measure for moving objects in real-world scene. In: Lecture Notes in Electrical Engineering, vol. 339, pp. 641–648 (2015)

  15. Chen, L., Özsu, M.Tamer: Robust and fast similarity search for moving object trajectories. In: Proceedings of the 2005 ACM SIGMOD International conference on Management of data, pp. 491-502. New York (2005)

  16. Lee, J., Han, J., Whang,K.: Trajectory clustering: A partition-and-group framework. In: Proceedings of the 2007 ACM SIGMOD International Conference, pp. 593–604. New York (2007)

  17. Kim, J., Mahmassani, H.S.: Spatial and temporal characterization of travel patterns in a traffic network using vehicle trajectories. Transp. Res. Proc. 59, 164–184 (2015)

    Article  Google Scholar 

  18. Deng, Z., Hu, Y., Zhu, M., Huang, X., Du, B.: A scalable and fast OPTICS for clustering trajectory big data. Clust. Comput. 18(2), 549–562 (2015)

    Article  Google Scholar 

  19. Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  MathSciNet  MATH  Google Scholar 

  20. Jim, Z.C., Juan, E.Y.T., Lai, F.J.C.: Rough clustering using generalized fuzzy clustering algorithm. Pattern Recogn. 46(9), 2538–2547 (2013)

    Article  Google Scholar 

  21. Treerattanapita, K., Jaruskulchai, C.: Possibilistic exponential fuzzy clustering. J. Comput. Sci. Technol. 28(2), 311–321 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  22. Ho, T.B., Nguyen, N.B.: Nonhierarchical document clustering based on a tolerance rough set model. Int. J. Intell. Syst. 17(2), 199–212 (2002)

    Article  MATH  Google Scholar 

  23. Kawasaki, S., Binh, N., Bao, T.: Hierarchical document clustering based on tolerance rough set model. In: Lecture Notes in Computer Science, vol. 1910, pp. 458–463 (2000)

  24. Patra, B.K., Nandi, S.: Tolerance rough set theory based data summarization for clustering large dataset. In: Transactions on Rough Sets XIV LNCS, vol. 6600, pp. 139–158 (2011)

  25. Wang, L., Song, W., Liu, P.: Link the remote sensing big data to the image features via wavelet transformation. Clust. Comput. 19(2), 793–810 (2016)

    Article  Google Scholar 

  26. Xuan, J., Luo, X., Zhang, G., Lu, J., Xu, Z.: Uncertainty analysis for the keyword system of web events. IEEE Trans. Syst. Man Cybern. Syst. 46(6), 829–842 (2016)

    Article  Google Scholar 

  27. Z. Xu et al. Crowdsourcing based description of urban emergency events using social media big data. IEEE Trans. Cloud Comput.. doi:10.1109/TCC.2016.2517638

  28. Pelekis, N., Kopanakis, L., Panagiotakis, C., Thedoridis, Y.: Unsupervised trajectory sample. In: Proceeding of ECML PKDD, Barcelona, pp. 17–33 (2010)

  29. Hai, P.N., Lenco, D., Poncelet, P., Teisseire, M.: Mining representative movement patterns through compression. In: Proceedings of 17\(^{th}\) Pacific-Asia Conference, PAKDD, Gold Coast, pp. 14–17 (2013)

  30. De, S.K., Krishna, P.R.: Clustering web transactions using rough approximation. Fuzzy Sets Syst. 148(1), 131–138 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  31. Pawlak, Z.: Rough sets. Int. J. Parallel Progr. 11(5), 341–356 (1982)

    MathSciNet  MATH  Google Scholar 

  32. Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.J.: Comparing images using the Hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 15(9), 850–863 (1993)

    Article  Google Scholar 

  33. Douglas, D.H., Peucker, T.K.: Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Can. Cartogr. 10(2), 112–122 (1973)

    Article  Google Scholar 

  34. Deng, M., Liu, Q., Li, G.: Spatial Clustering Analysis and Application. Science Press, Beijing (2011)

    Google Scholar 

  35. Xie, X.L., Beni, G.: A validity measure for fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell. 13(8), 841–847 (1991)

    Article  Google Scholar 

Download references

Acknowledgments

This research work was supported by research project of National Natural Science Foundation under Grant 41401452 and 91224008.

Author information

Authors and Affiliations

  1. School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, 430079, Wuhan, China

    Chunchun Hu, Nianxue Luo & Qiansheng Zhao

Authors
  1. Chunchun Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nianxue Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qiansheng Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chunchun Hu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hu, C., Luo, N. & Zhao, Q. Fast fuzzy trajectory clustering strategy based on data summarization and rough approximation. Cluster Comput 19, 1411–1420 (2016). https://doi.org/10.1007/s10586-016-0603-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-016-0603-8

Keywords

Search

Navigation