The week's pick
Reseach Article
Framework for Social Network Data Mining
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 116 - Number 18 |
| Year of Publication: 2015 |
| Authors: Gayana Fernando, Md Gapar, Mdjohar |
10.5120/20434-2765
|
Gayana Fernando, Md Gapar, Mdjohar . Framework for Social Network Data Mining. International Journal of Computer Applications. 116, 18 ( April 2015), 7-10. DOI=10.5120/20434-2765
Abstract
Social networks have become a vital component in personal life. People are addicted to social network features, updating their profile page and collaborating virtually with other members have become daily routines. Social networks contain massive collection of data. Web data mining is a new trend in the current research body. This conceptual paper introduces a framework that can be used to mine social network data. The proposed framework tries to handle the major limitations in current web mining frameworks by handling the unstructured and dynamic behavior of web data. Framework adopts the Hidden Markov Model to the data mining algorithm to predict the next status of web data.
References
- E. Ackerman and E. Guizzo, '5 technologies that will shape the web', IEEE Spectr. , vol. 48, no. 6, pp. 40-45, 2011.
- F. Bonchi, C. Castillo, A. Gionis and A. Jaimes, 'Social Network Analysis and Mining for Business Applications', ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, pp. 1-37, 2011.
- S. G. S. FernandoG. Johar, and S. N. Perera. "Empirical Analysis of Data Mining Techniques for Social Network Websites. ", An Internatational Journal of Advance computer Technology, Vol 3, February 2014.
- Clemons, K. Eric, S. Barnett, and A. Appadurai. "The future of advertising and the value of social network websites: some preliminary examinations. " Proceedings of the ninth international conference on Electronic commerce. ACM, 2007.
- M. Hao, H. Yang, M. R. Lyu, and I. King. "Mining social networks using heat diffusion processes for marketing candidates selection. " Proceedings of the 17th ACM conference on Information and knowledge management, pp. 233-242. ACM, 2008.
- Tucker, E Catherine. "Social networks, personalized advertising, and privacy controls. " Journal of Marketing Research 51, no. 5 (2014): 546-562.
- J. Srivastava, R. Cooley, M. Deshpande and P. Tan, 'Web usage mining', ACM SIGKDD Explorations Newsletter, vol. 1, no. 2, p. 12, 2000.
- Z. Markov and D. Larose, Data mining the Web. Hoboken, N. J. : Wiley-Interscience, 2007.
- H. Yanagimoto, M. Yoshioka, and S. Omatu. "Web clustering using social bookmarking data with dimension reduction regarding similarity. " Advances in Social Networks Analysis and Mining (ASONAM), 2010 International Conference on, pp. 386-390. IEEE, 2010.
- S. Rangarajan, V. Phoha, K. Balagani, R. Selmic and S. Iyengar, 'Adaptive neural network clustering of Web users', Computer, vol. 37, no. 4, pp. 34-40, 2004.
- S. R. Aghabozorgi , and T. Y. Wah. "Using incremental fuzzy clustering to web usage mining. "Soft Computing and Pattern Recognition, 2009. SOCPAR'09. International Conference of, pp. 653-658. IEEE, 2009.
- Wikipedia, 'Hidden Markov model', 2015. [Online]. Available: http://en. wikipedia. org/wiki/Hidden_Markov_model. [Accessed: 14- Apr- 2015].
- Da Silva, G. Aires, and D. R. Ferreira. "Applying hidden Markov models to process mining. " Sistemas e Tecnologias de Informação: Actas da 4ª ConferênciaIbérica de Sistemas e Tecnologias de Informação, AISTI/FEUP/UPF. 2009.
- C. Souza, 'Sequence Classifiers in C# - Part I: Hidden Markov Models - CodeProject', Codeproject. com, 2014. [Online]. Available: http://www. codeproject. com/Articles/541428/Sequence-Classifiers-in-Csharp-Part-I-Hidden-Marko. [Accessed: 14- Apr- 2015].
- M. Zaki, C. Carothers and B. Szymanski, 'VOGUE', ACM Transactions on Knowledge Discovery from Data, vol. 4, no. 1, pp. 1-31, 2010.
- M. Andrea. "Hidden Markov Models applied to Data Mining. " (2006).
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