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The application of social network mining to cattle movement analysis: introducing the predictive trend mining framework

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Abstract

This paper describes a predictive social network mining framework which is demonstrated using the Great Britain cattle movement datasets. The proposed framework, the predictive trend mining framework (PTMF), is used to analyse episodes of time-stamped social network data. The PTMF has two main components (1) a frequent pattern trend analysis component that efficiently identifies temporal frequent patterns and trends and also provides a mechanism for clustering and analysing these patterns and trends so as to detect dynamic changes within the cattle movement network, and (2) the predictive modelling component for forecasting the percolation of information or data across the network. The PTMF incorporates a number of novel elements including mechanisms to: (1) identify temporal frequent patterns and trends, (2) cluster large sets of trends, (3) analyse temporal clusters for pattern trend change detection, (4) visualise these changes using pattern migration network maps and (5) predict the paths whereby information moves across the network over time.

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Authors and Affiliations

  1. Institute of Visual Informatics, Universiti Kebangsaan Malaysia, Bangi, Malaysia

    Puteri Nohuddin

  2. Department of Computer Science, University of Liverpool, Liverpool, UK

    Frans Coenen

  3. Department of Epidemiology and Population Health, Institute of Infection and Global Health, University of Liverpool, Liverpool, UK

    Rob Christley

Authors
  1. Puteri Nohuddin
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  2. Frans Coenen
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  3. Rob Christley
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Correspondence to Puteri Nohuddin.

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Nohuddin, P., Coenen, F. & Christley, R. The application of social network mining to cattle movement analysis: introducing the predictive trend mining framework. Soc. Netw. Anal. Min. 6, 45 (2016). https://doi.org/10.1007/s13278-016-0353-x

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  • DOI: https://doi.org/10.1007/s13278-016-0353-x

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