Abstract
This paper presents the Shape based Movement Pattern (ShaMP) algorithm, an algorithm for extracting Movement Patterns (MPs) from network data that can later be used (say) for prediction purposes. The principal advantage offered by the ShaMP algorithm is that it lends itself to parallelisation so that very large networks can be processed. The concept of MPs is fully defined together with the realisation of the ShaMP algorithm. The algorithm is evaluated by comparing its operation with a benchmark Apriori based approach, the Apriori based Movement Pattern (AMP) algorithm, using large social networks generated from the Cattle tracking Systems (CTS) in operation in Great Britain (GB) and artificial networks.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Matsumura, N., Goldberg, D.E., Llorà, X.: Mining directed social network from message board. In: Special Interest Tracks and Posters of the 14th International Conference on World Wide Web, pp. 1092–1093. ACM (2005)
Chandrasekaran, B.: Survey of Network Traffic Models, vol. 567. Washington University, St. Louis CSE (2009)
Datta, S., Bhaduri, K., Giannella, C., Wolff, R., Kargupta, H.: Distributed data mining in peer-to-peer networks. IEEE Internet Comput. 10(4), 18–26 (2006)
Gonzalez, H., Han, J., Li, X., Myslinska, M., Sondag, J.P.: Adaptive fastest path computation on a road network: a traffic mining approach. In: Proceedings of the 33rd International Conference on Very Large Data Bases, VLDB Endowment, pp. 794–805 (2007)
Galloway, J., Simoff, S.J.: Network data mining: methods and techniques for discovering deep linkage between attributes. In: Proceedings of the 3rd Asia-Pacific Conference on Conceptual Modelling, vol. 53, pp. 21–32. Australian Computer Society, Inc. (2006)
Grama, A.: Introduction to Parallel Computing. Pearson Education (2003)
Raorane, A., Kulkarni, R.: Data mining techniques: a source for consumer behavior analysis (2011). arXiv:1109.1202
Gudmundsson, J., Laube, P., Wolle, T.: Movement patterns in spatio-temporal data. In: Encyclopedia of GIS, pp. 726–732. Springer (2008)
Campbell, W.M., Dagli, C.K., Weinstein, C.J.: Social network analysis with content and graphs. Lincoln Lab. J. 20(1) (2013)
Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Elsevier (2011)
Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proceedings of 20th International Conference on Very Large Data Bases, VLDB, vol. 1215, pp. 487–499 (1994)
Bliss, C.A., Frank, M.R., Danforth, C.M., Dodds, P.S.: An evolutionary algorithm approach to link prediction in dynamic social networks. J. Comput. Sci. 5(5), 750–764 (2014)
Kim, M., Leskovec, J.: The network completion problem: Inferring missing nodes and edges in networks. In: SDM, vol. 11, pp. 47–58. SIAM (2011)
Forum, M.P.I.: Mpi: a message passing interface standard: version 2.2; message passing interface forum, September 4, 2009
Brawer, S.: Introduction to Parallel Programming. Academic Press (2014)
Gropp, W., Lusk, E., Doss, N., Skjellum, A.: A high-performance, portable implementation of the mpi message passing interface standard. Parallel Comput. 22(6), 789–828 (1996)
Karonis, N.T., Toonen, B., Foster, I.: Mpich-g2: a grid-enabled implementation of the message passing interface. J. Parallel Distrib. Comput. 63(5), 551–563 (2003)
Chen, L., Wang, C., Lau, F.C.: A grid middleware for distributed java computing with mpi binding and process migration supports. J. Comput. Sci. Technol. 18(4), 505–514 (2003)
Baker, M., Carpenter, B., Shaft, A.: Mpj express: towards thread safe java hpc. In: 2006 IEEE International Conference on Cluster Computing, pp. 1–10. IEEE (2006)
Aggarwal, C.C.: Applications of frequent pattern mining. In: Frequent Pattern Mining, pp. 443–467. Springer (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Al-Zeyadi, M., Coenen, F., Lisitsa, A. (2016). Mining Frequent Movement Patterns in Large Networks: A Parallel Approach Using Shapes. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXXIII. SGAI 2016. Springer, Cham. https://doi.org/10.1007/978-3-319-47175-4_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-47175-4_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47174-7
Online ISBN: 978-3-319-47175-4
eBook Packages: Computer ScienceComputer Science (R0)