Abstract
Link mining is also called social network analysis. It is a new study of data mining. It is different from the traditional data mining methods. Link information is used in link mining. Link information provides richer and more accurate information about the social network. In this paper, a representation is chosen by Graph, Dyad and Subgraph for the statistical inference of mining. And then based on the defining of the graph structure and link type, the model of getting the link features is built. Last a combining link-based and content-based classification method is proposed, and this method is proved to improve the result of classification.
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References
Han, J.W., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2000)
Chakrabarti, S.: Mining the Web. Morgan Kaufman, San Francisco (2000)
Freeman, L.: The Development of Social Network Analysis. In: A Study in the Sociology of Science 2004. Empirical Press, Vancouver (2004)
Cook, D., Holder, L.: Graph-based data mining. IEEE Intelligent Systems and Their Applications 15(2), 32–41 (2000)
Mccallum, A., Nigam, K.: A comparison of event models for Naïve Bayes text classification. In: Proceeding of AAAI/ICML-1998 Workshop on Principles of Database Systems, pp. 41–48 (1998)
Kantarcioglu, M., Clifton, C.: Privacy preserving k-nn classifier. In: ICDE (2005)
Osuna, E., Freund, R., Girosi, F.: An improved training algorithm for support vector machines. In: Neural Networks for Signal Processing VII–Proceedings of the 1997 IEEE Workshop, pp. 276–285 (1997)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible inference. Morgan Kaufmanns, San Francisco (1988)
Ribeiro, N.B., Muntz, R.: A belief network model for IR. In: Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Zurich, pp. 253–260 (1996)
Turtle, H., Croft, W.B.: Evaluation of inference network-based retrieval model. ACM Transactions on Information Systems 9(3), 187–222 (1991)
Ribeiro, N.B., Silva, I., Muntz, R.: Soft Computing in Information Retrieval: Techniques and Applications. In: Chapter 11-Bayesian Network Models for IR, pp. 259–291. Springer, Heidelberg (2000)
Mccallum, A., Nigam, K., Rennie, J., et al.: Automating the Construction of Internet Portals with Machine Learning. Information Retrieval 3(2), 127–163 (2000)
Craven, M., Dipasquo, D., Freitag, D., et al.: Learning to extract symbolic knowledge from the world wide web. In: Madison (ed.) 15th Conference of the American Association for Artificial Intelligence 1998, pp. 509–516. AAAI Press, Menlo Park (1998)
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Tian, K. (2011). Combining Link-Based and Content-Based Classification Method. In: Gong, Z., Luo, X., Chen, J., Lei, J., Wang, F.L. (eds) Web Information Systems and Mining. WISM 2011. Lecture Notes in Computer Science, vol 6988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23982-3_21
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DOI: https://doi.org/10.1007/978-3-642-23982-3_21
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