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Social Network Analysis and Mining
Published in:

01-12-2016 | Original Article

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

Authors: Puteri Nohuddin, Frans Coenen, Rob Christley

Published in: Social Network Analysis and Mining | Issue 1/2016

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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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Metadata
Title
The application of social network mining to cattle movement analysis: introducing the predictive trend mining framework
Authors
Puteri Nohuddin
Frans Coenen
Rob Christley
Publication date
01-12-2016
Publisher
Springer Vienna
Published in
Social Network Analysis and Mining / Issue 1/2016
Print ISSN: 1869-5450
Electronic ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-016-0353-x

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