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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aggrawal, C., Yu, P.: A Condensation Approach to Privacy Preserving Data Mining. In: Bertino, E., Christodoulakis, S., Plexousakis, D., Christophides, V., Koubarakis, M., Böhm, K., Ferrari, E. (eds.) EDBT 2004. LNCS, vol. 2992, pp. 183–199. Springer, Heidelberg (2004)
Read, J.M., Eames, K.T.D., Edmunds, W.J.: Dynamic Social Networks and the implications for the spead of infectious disease. J. R. Soc. Interface 5, 1001–1007 (2008)
Chalabi, Z., Berrached, N., Kharchouche, N., Ghellemallah, Y., Mansour, M., Mouhadjer, H.: Classification of the Medical Images by the Kohonen Network SOM and LVQ. Journal of Applied Sciences 8(7), 1149–1158 (2008)
Coenen, F.P., Leng, P., Ahmed, S.: Data Structures for association Rule Mining: T-trees and P-trees. IEEE Transactions on Data and Knowledge Engineering 16(6), 774–778 (2004)
Coenen, F.P., Goulbourne, G., Leng, P.: Computing Association Rules Using Partial Totals. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 54–66. Springer, Heidelberg (2001)
Cottrell, M., Rousset, P.: A powerful Tool for Analyzing and Representing Multidimensional Quantitative and Qualitative Data. In: Cabestany, J., Mira, J., Moreno-Díaz, R. (eds.) IWANN 1997. LNCS, vol. 1240, pp. 861–871. Springer, Heidelberg (1997)
Domingos, P.: Mining Social Networks for Viral Marketing. IEEE Intelligent Systems 20(1), 80–82 (2005)
Green, D.M., Kao, R.R.: Data quality of the Cattle Tracing System in Great Britain. Veterinary Record 161(13), 439–443 (2007)
Khan, M.S., Coenen, F., Reid, D., Tawfik, H., Patel, R., Lawson, A.: A Sliding Windows based Dual Support Framework for Discovering Emerging Trends from Temporal Data. KBS Journal (to be appeared 2010)
Kohonen, T.: The Self Organizing Maps. Neurocomputing 21, 1–6 (1998)
Kohonen, T.: The Self Organizing Maps. Series in Information Sciences, vol. 30. Springer, Heidelberg (1995)
Kohonen, T., Oja, E., Simula, O., Visa, A., Kangas, J.: Engineering applications of the Self-Organizing Map. Proceedings of the IEEE 84(10), 1358–1384 (1996)
Krause, G., Blackmore, C., Wiersma, S., Lesneski, C., Woods, C.W., Rosenstein, N.E., Hopkins, R.S.: Marijuana use and Social Networks in a Community Outbreak of Meningococcal Disease. South Medical Journal 94(5), 482–485 (2001)
Lauw, H., Lim, E., Pang, H., Tan, T.: Social Network Discovery by Mining Spatio-Temporal Events. Computational & Mathematical Organization Theory 11(2), 97–118 (2005)
McCallum, A., Corrada-Emmanuel, A., Wang, X.: Topic and Role Discovery in Social Networks with Experiments on Enron and Academic Email. Journal of Artificial Intelligence Research 30, 249–272 (2007)
Mitchell, A., Bourn, D., Mawdsley, J., Wint, W., Clifton-Hadley, R., Gilbert, M.: Characteristics of cattle movements in Britain: An analysis of records from the Cattle Tracing System. Animal Science 80, 265–273 (2005)
Robinson, S., Christley, R.M.: Identifying temporal variation in reported births, deaths and movements of cattle in Britain. BMC Veterinary Research, 2–11 (2006)
Streibel, O.: Trend Mining with Semantic-Based Learning. In: Proceedings of CAiSE-DC (2008)
Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: Self-Organizing Map in Matlab: the SOM Toolbox. In: Proceedings of the Matlab DSP Conference (200)
Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (2006)
Defra. Livestock movements, identification and tracing: Cattle Tracing System, http://www.defra.gov.uk/foodfarm/farmanimal/movements/cattle/cts.htm
Google Trends, http://www.google.com/intl/en/trends/about.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nohuddin, P.N.E., Christley, R., Coenen, F., Setzkorn, C. (2010). Trend Mining in Social Networks: A Study Using a Large Cattle Movement Database. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2010. Lecture Notes in Computer Science(), vol 6171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14400-4_36
Download citation
DOI: https://doi.org/10.1007/978-3-642-14400-4_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14399-1
Online ISBN: 978-3-642-14400-4
eBook Packages: Computer ScienceComputer Science (R0)