Abstract
Analyzing trajectories is important and has many applications, such as surveillance, analyzing traffic patterns and hurricane path prediction. In this paper, we propose a unique, non-parametric trajectory density estimation approach to obtain trajectory density functions that are used for two purposes. First, a density-based clustering algorithm DENTRAC that operates on such density functions is introduced. Second, unique post-analysis techniques that use the trajectory density function are proposed. Our method is capable of ranking trajectory clusters based on different characteristics of density clusters, and thus has the ability to summarize clusters from different perspectives, such as the compactness of member trajectories or the probability of their occurrence. We evaluate the proposed methods on synthetic traffic and real-world Atlantic hurricane datasets. The results show that our simple, yet effective approach extracts valuable knowledge from trajectories that is difficult to obtain with other approaches.
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References
Alt, H., Behrends, B., Blömer, J.: Approximate matching of polygonal shapes. Annals of Mathematics and Artificial Intelligence 13, 251–265 (1995)
Alt, H., Knauer, C., Wenk, C.: Matching polygonal curves with respect to the fréchet distance. In: Ferreira, A., Reichel, H. (eds.) STACS 2001. LNCS, vol. 2010, pp. 63–74. Springer, Heidelberg (2001)
Bagherjeiran, A., Celepcikay, O.U., Jiamthapthaksin, R., Chen, C.-S., Rinsurongkawong, V., Lee, S., Thomas, J., Eick, C.F.: Cougarˆ2: An open source machine learning and data mining development framework. In: Proc. Open Source Data Mining Workshop OSDM 2009 (April 2009)
Brinkhoff, T., Str, O.: A framework for generating network-based moving objects. Geoinformatica 6 (2002)
Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise, pp. 226–231. AAAI Press, Menlo Park
Hinneburg, E., Gabriel, H.-h.: Denclue 2.0: Fast clustering based on kernel density estimation. In: Proc. The 7th International Symposium on Intelligent Data Analysis, pp. 70–80 (2007)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: an Introduction to Cluster Analysis. John Wiley & Sons, Chichester (1990)
Lee, J.-G., Han, J., Whang, K.-Y.: Trajectory clustering: a partition-and-group framework. In: Proc. the 2007 ACM SIGMOD International Conference on Management of Data SIGMOD 2007, pp. 593–604 (2007)
Morris, B., Trivedi, M.: Learning trajectory patterns by clustering: Experimental studies and comparative evaluation. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition CVPR 2009, pp. 312–319 (2009)
Nanni, M.: Clustering methods for spatio-temporal data. Phd thesis, CS Department, University of Pisa, Italy (2002)
Nanni, M., Pedreschi, D.: Time-focused clustering of trajectories of moving objects. J. Intell. Inf. Syst. 27(3), 267–289 (2006)
Palma, A.T., Bogorny, V., Kuijpers, B., Alvares, L.O.: A clustering-based approach for discovering interesting places in trajectories. In: Proc. the 2008 ACM Symposium on Applied Computing SAC 2008, pp. 863–868 (2008)
Pelekis, N., Kopanakis, I., Kotsifakos, E., Frentzos, E., Theodoridis, Y.: Clustering trajectories of moving objects in an uncertain world. In: Proc. Ninth IEEE International Conference on Data Mining ICDM 2009, pp. 417–427 (2009)
Zhang, Y., Pi, D.: A trajectory clustering algorithm based on symmetric neighborhood. In: Proc. WRI World Congress on Computer Science and Information Engineering, vol. 3, pp. 640–645 (2009)
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Chen, CS., Eick, C.F., Rizk, N.J. (2011). Mining Spatial Trajectories Using Non-parametric Density Functions. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2011. Lecture Notes in Computer Science(), vol 6871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23199-5_37
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DOI: https://doi.org/10.1007/978-3-642-23199-5_37
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