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

Nohuddin Puteri Nor Ellyza, Frans Coenen, Rob Christley

Research output: Contribution to journalArticle

3 Citations (Scopus)

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.

Original languageEnglish
Article number45
JournalSocial Network Analysis and Mining
Volume6
Issue number1
DOIs
Publication statusPublished - 1 Dec 2016

Fingerprint

social network
trend
migration

Keywords

  • Prediction
  • Social network mining
  • Trend mining and analysis

ASJC Scopus subject areas

  • Computer Science Applications
  • Human-Computer Interaction
  • Information Systems
  • Communication
  • Media Technology

Cite this

The application of social network mining to cattle movement analysis : introducing the predictive trend mining framework. / Puteri Nor Ellyza, Nohuddin; Coenen, Frans; Christley, Rob.

In: Social Network Analysis and Mining, Vol. 6, No. 1, 45, 01.12.2016.

Research output: Contribution to journalArticle

@article{de9dae3791cc48a9aed22cbf2095c54c,
title = "The application of social network mining to cattle movement analysis: introducing the predictive trend mining framework",
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.",
keywords = "Prediction, Social network mining, Trend mining and analysis",
author = "{Puteri Nor Ellyza}, Nohuddin and Frans Coenen and Rob Christley",
year = "2016",
month = "12",
day = "1",
doi = "10.1007/s13278-016-0353-x",
language = "English",
volume = "6",
journal = "Social Network Analysis and Mining",
issn = "1869-5450",
publisher = "Springer Wien",
number = "1",

}

TY - JOUR

T1 - The application of social network mining to cattle movement analysis

T2 - introducing the predictive trend mining framework

AU - Puteri Nor Ellyza, Nohuddin

AU - Coenen, Frans

AU - Christley, Rob

PY - 2016/12/1

Y1 - 2016/12/1

N2 - 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.

AB - 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.

KW - Prediction

KW - Social network mining

KW - Trend mining and analysis

UR - http://www.scopus.com/inward/record.url?scp=84977522216&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84977522216&partnerID=8YFLogxK

U2 - 10.1007/s13278-016-0353-x

DO - 10.1007/s13278-016-0353-x

M3 - Article

AN - SCOPUS:84977522216

VL - 6

JO - Social Network Analysis and Mining

JF - Social Network Analysis and Mining

SN - 1869-5450

IS - 1

M1 - 45

ER -