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Mining Frequent Movement Patterns in Large Networks: A Parallel Approach Using Shapes

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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.

Keywords

  • Knowledge Discovery and Data Mining
  • Distributed AI Algorithms
  • Systems and Applications

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Correspondence to Mohammed Al-Zeyadi .

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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

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  • DOI: https://doi.org/10.1007/978-3-319-47175-4_4

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