Advertisement

Clustering of Trajectory Data Using Hierarchical Approaches

  • B. A. SabarishEmail author
  • R. Karthi
  • T. Gireeshkumar
Conference paper
First Online:
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 28)

Abstract

Large volume of spatiotemporal data as trajectories are generated from GPS enabled devices such as smartphones, cars, sensors, and social media. In this paper, we present a methodology for clustering of trajectories to identify patterns in vehicle movement. The trajectories are clustered using hierarchical method and similarity between trajectories are computed using Dynamic Time Warping (DTW) measure. We study the effects on clustering by varying the linkage methods used for clustering of trajectories. The clustering method generate clusters that are spatially similar and optimal results are obtained during the clustering process. The results are validated using Cophenetic correlation coefficient, Dunn, and Davies-Bouldin Index by varying the number of clusters. The results are tested for its efficiency using real world data sets. Experimental results demonstrate that hierarchical clustering using DTW measure can cluster trajectories efficiently.

Keywords

Trajectory Dynamic time warping distance Hierarchical clustering Linkage methods 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Mazimpaka, J.D., Timpf, S.: Trajectory data mining: a review of methods and applications. J. Spat. Inf. Sci. 61–99 (2016)Google Scholar
  2. 2.
    Zheng, Y.: Trajectory data mining: an overview. In: ACM Transactions on Intelligent Systems and Technology (TIST) (2015)Google Scholar
  3. 3.
    Praveen, V., Sivakumar, P.B.: Design of IoT systems and analytics in the context of smart city initiatives in India. Procedia Comput. Sci. 92, 583–588 (2016)CrossRefGoogle Scholar
  4. 4.
    Feng, Z., Zhu, Y.: A survey on trajectory data mining: techniques and applications. IEEE Access 2056–2067 (2016)Google Scholar
  5. 5.
    Lee, J.G., Han, J., Li, X., Gonzalez, H.: TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering. In: Proceedings of the VLDB Endowment, pp. 1081–1094 (2008)Google Scholar
  6. 6.
    Gaffney, S., Smyth, P.: Trajectory clustering with mixtures of regression models. In: Proceedings of 50th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 63–72 (1999)Google Scholar
  7. 7.
    Chih, H.C., Peng, W.C., Lee, W.C.: Clustering and aggregating clues of trajectories for mining trajectory patterns and routes. VLDB J. 24, 169–192 (2015)CrossRefGoogle Scholar
  8. 8.
    Gudmundsson, J., Andreas, T., Jan, V.: Of Motifs and Goals: Mining Trajectory Data, ACM SIGSPATIAL GIS ’12, pp. 129–138 (2012)Google Scholar
  9. 9.
    Kim, J., Mahmassani, H.S.: Spatial and temporal characterization of travel patterns in a traffic network using vehicle trajectories. Transp. Res. C Emerg. Technol. 375–390 (2015)Google Scholar
  10. 10.
    Vlachos, M., Kollios, G., Gunopulos, D.: Discovering similar multidimensional trajectories. 18th International Conference on Data Engineering, pp. 673–684 (2002)Google Scholar
  11. 11.
    Wang, H., Su, H., Zheng, K., Sadiq, S., Zhou, X.: An effectiveness study on trajectory similarity measures. In: Proceedings of the Twenty-Fourth Australasian Database Conference, pp. 13–22 (2013)Google Scholar
  12. 12.
    Besse, P., Guillouet, B., Loubes, J.M., François, R.: Review and perspective for distance based trajectory clustering of vehicle trajectories. IEEE Trans. Intell. Transp. Syst. 17(11), 3306–3317 (2016)Google Scholar
  13. 13.
    Zhao, Q., Shi, Y., Liu, Q., Fränti, P.: A grid-growing clustering algorithm for geo-spatial data. Pattern Recognit. Lett. 53, 77–84 (2015)CrossRefGoogle Scholar
  14. 14.
    Douglas, D., Peucker, T.: Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Can. Cartographer 10(2), 112–122 (1973)CrossRefGoogle Scholar
  15. 15.
    Zheng, Y., Zhou, X.: Computing with spatial trajectories. Springer, New York (2011)Google Scholar
  16. 16.
    Yuan, G., Sun, P., Zhao, J., Li, D., Wang, C.: A review of moving object trajectory clustering algorithms. Artif. Intell. Rev. 77, 123–144 (2017)CrossRefGoogle Scholar
  17. 17.
    Xu, R., Wunsch, D.: Survey of clustering algorithms. IEEE Trans. Neural Netw. 16(3), 645–678 (2005)CrossRefGoogle Scholar
  18. 18.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2012)Google Scholar
  19. 19.
    Sabarish, B.A., Karthi, R., Gireeshkumar, T.: A survey of location prediction using trajectory mining. Artificial Intelligence and Evolutionary Algorithms in Engineering Systems Springer India, pp. 119–127 (2015)Google Scholar
  20. 20.
    Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On clustering validation techniques. J. Intell. Inf. Syst. 17(2), 107–145 (2011)Google Scholar
  21. 21.
    Sinan, S., Nurhan, D., Ismet, D.: Comparison of hierarchical cluster analysis methods by cophenetic correlation. J. Inequalities Appl. 2013(203), 1–8 (2013)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG  2018

Authors and Affiliations

  1. 1.Dept of Computer Science and EngineeringAmrita School of Engineering, Amrita Vishwa VidyapeethamCoimbatoreIndia
  2. 2.TIFAC CORE in Cyber SecurityAmrita School of Engineering, Amrita Vishwa VidyapeethamCoimbatoreIndia

Personalised recommendations

Cite paper

Buy options