Abstract
Sharing of user data has substantially increased over the past few years facilitated by sophisticated Web and mobile applications, including social networks. For instance, users can easily register their trajectories over time based on their daily trips captured with GPS receivers as well as share and relate them with trajectories of other users. Analyzing user trajectories over time can reveal habits and preferences. This information can be used to recommend content to single users or to group users together based on similar trajectories and/or preferences. Recording GPS tracks generates very large amounts of data. Therefore clustering algorithms are required to efficiently analyze such data. In this paper, we focus on investigating ways of efficiently analyzing user trajectories and extracting user preferences from them. We demonstrate an algorithm for clustering user GPS trajectories. In addition, we propose an algorithm to correlate trajectories based on near points between two or more users. The obtained results provided interesting avenues for exploring Location-based Social Network (LBSN) applications.
Research presented in this paper was funded by a Strategic Research Cluster grant (07/SRC/I1168) by Science Foundation Ireland (SFI) under the National Development Plan, the IRCSET Ulysses program, French Ministry of Higher Education and Research, ÉGIDE program and European Cooperation in Science and Technology (COST). The authors gratefully acknowledge this support.
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References
Braga, R.B., Martin, H.: Captain: A context-aware system based on personal tracking. In: The 17th International Conference on Distributed Multimedia Systems / DMS 2011. KSI, Florence (2011)
Wu, Q., Huang, B., Tay, R.: Adaptive Path Finding for Moving Objects. In: Li, K.-J., Vangenot, C. (eds.) W2GIS 2005. LNCS, vol. 3833, pp. 155–167. Springer, Heidelberg (2005)
Pfoser, D., Brakatsoulas, S., Brosch, P., Umlauft, M., Tryfona, N., Tsironis, G.: Dynamic travel time provision for road networks. In: The 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS 2008, pp. 68:1–68:4. ACM, New York (2008)
Yuan, J., Zheng, Y., Zhang, C., Xie, W., Xie, X., Sun, G., Huang, Y.: T-drive: driving directions based on taxi trajectories. In: The 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS 2010, pp. 99–108. ACM, New York (2010)
Andersen, R., Borgs, C., Chayes, J., Feige, U., Flaxman, A., Kalai, A., Mirrokni, V., Tennenholtz, M.: Trust-based recommendation systems: an axiomatic approach. In: The 17th International Conference on World Wide Web, WWW 2008, pp. 199–208. ACM, New York (2008)
Cavanagh, A.: From culture to connection: Internet community studies. Sociology Compass 3, 1–15 (2009)
Online Conference on Networks and Communities: Are virtual communities a good thing socially? (2010), http://networkconference.netstudies.org (last access: October 27, 2011)
Han, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc., San Francisco (2005)
Morris, B., Trivedi, M.: Learning trajectory patterns by clustering: Experimental studies and comparative evaluation. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 312–319 (2009)
Rinzivillo, S., Pedreschi, D., Nanni, M., Giannotti, F., Andrienko, N., Andrienko, G.: Visually driven analysis of movement data by progressive clustering. Information Visualization 7, 225–239 (2008)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: The 2nd International Conference on Knowledge Discovery and, pp. 226–231 (1996)
Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: OPTICS: Ordering Points to Identify the Clustering Structure. SIGMOD Rec. 28, 49–60 (1999)
Tahir, G., McArdle, M.B.: Visualising user interaction history to identify web map usage patterns. In: 14th AGILE International Conference on Geographic Information Science, Advancing Geoinformation Science for a Changing World, Utrecht, The Netherlands (2011)
Andrienko, G., Andrienko, N., Wrobel, S.: Visual analytics tools for analysis of movement data. SIGKDD Explorations Newsletter - Special Issue on Visual Analytics 9, 38–46 (2007)
Points of Interest Working Group: W3c points of interest working group charter (2011), http://www.w3.org/2010/POI/charter/ (last access: October 27, 2011)
Benevenuto, F., Rodrigues, T., Cha, M., Almeida, V.: Characterizing user behavior in online social networks. In: The 9th ACM SIGCOMM Conference on Internet Measurement, IMC 2009, pp. 49–62. ACM, New York (2009)
MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Cam, L.M.L., Neyman, J. (eds.) The 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press (1967)
Papadias, D., Sellis, T., Theodoridis, Y., Egenhofer, M.J.: Topological relations in the world of minimum bounding rectangles: a study with r-trees. In: ACM SIGMOD International Conference on Management of Data, vol. 24, pp. 92–103 (1995)
Atallah, M.J.: A linear time algorithm for the hausdorff distance between convex polygons. Informatics Processing Letters 17, 207–209 (1983)
Jacox, E.H., Samet, H.: Metric space similarity joins. ACM Transaction on Database Systems 33, 7:1–7:38 (2008)
Bischoff, S., Pavic, D., Kobbelt, L.: Automatic restoration of polygon models. ACM Transactions on Graphics 24, 1332–1352 (2005)
Brecheisen, S., Kriegel, H., Kröger, P., Pfeifle, M.: Visually mining through cluster hierarchies. In: International Conference on Data Mining, Citeseer, Orlando, FL (2004)
Gross, R., Acquisti, A.: Information revelation and privacy in online social networks. In: Proceedings of the 2005 ACM Workshop on Privacy in the Electronic Society, WPES 2005, pp. 71–80. ACM, New York (2005)
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Braga, R.B., Tahir, A., Bertolotto, M., Martin, H. (2012). Clustering User Trajectories to Find Patterns for Social Interaction Applications. In: Di Martino, S., Peron, A., Tezuka, T. (eds) Web and Wireless Geographical Information Systems. W2GIS 2012. Lecture Notes in Computer Science, vol 7236. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29247-7_8
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DOI: https://doi.org/10.1007/978-3-642-29247-7_8
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