Trend mining in social networks: a study using a large cattle movement database

Authors:
Puteri N. E. Nohuddin;Rob Christley;Frans Coenen;Christian Setzkorn
Affiliations:
Department of Computer Science, University of Liverpool, UK;School of Veterinary Science, University of Liverpool and National Centre for Zoonosis Research, Neston, UK;Department of Computer Science, University of Liverpool, UK;School of Veterinary Science, University of Liverpool and National Centre for Zoonosis Research, Neston, UK
Venue:
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Year:
2010

Citing 7
Cited 2

Self-Organizing Maps

Self-Organizing Maps
Computing Association Rules Using Partial Totals

PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
The Kohonen Algorithm: A Powerful Tool for Analyzing and Representing Multidimensional Quantitative and Qualitative Data

IWANN '97 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Biological and Artificial Computation: From Neuroscience to Technology
Social Network Discovery by Mining Spatio-Temporal Events

Computational & Mathematical Organization Theory
Data Structure for Association Rule Mining: T-Trees and P-Trees

IEEE Transactions on Knowledge and Data Engineering
Topic and role discovery in social networks with experiments on enron and academic email

Journal of Artificial Intelligence Research
A sliding windows based dual support framework for discovering emerging trends from temporal data

Knowledge-Based Systems

Finding temporal patterns in noisy longitudinal data: a study in diabetic retinopathy

ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Identification and visualisation of pattern migrations in big network data

PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper reports on a mechanism to identify temporal spatial trends in social networks. The trends of interest are defined in terms of the occurrence frequency of time stamped patterns across social network data. The paper proposes a technique for identifying such trends founded on the Frequent Pattern Mining paradigm. The challenge of this technique is that, given appropriate conditions, many trends may be produced; and consequently the analysis of the end result is inhibited. To assist in the analysis, a Self Organising Map (SOM) based approach, to visualizing the outcomes, is proposed. The focus for the work is the social network represented by the UK's cattle movement data base. However, the proposed solution is equally applicable to other large social networks.