Abstract
In the vast data ocean, discovering and using the valuable information has become the key technology. The data mining is the powerful tool to solve this problem. In this paper, the commonly used data mining technology is introduced, and the current popular four Web database technologies are analyzed, and the data mining model that is suitable for comprehensive Web database is put forward finally. To sum up the above, it has certain theoretical research and practical application value.
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References
Han, J., Kamber, M.: Concepts and Technologies of Data Mining, vol. 13(4), pp. 75–79. Mechanical Industry Press, Beijing (2001)
Kantardzic, M.: Data mining-concept, model, method and algorithm, vol. 13(5), pp. 244–249. Tsinghua University Press, Beijing (2004)
Steinberg, D., Cart, C.P.L.: Tree-structured nonparametric data analysis. Salford Systems 6(7), 46–52 (1995)
Lv, A., Lin, Z., Li, C.: Technique Methods of Data Mining and KDD. Science of Surveying and Mapping 25(4), 36–39 (2000)
Kantarcioglu, M., Clifton, C.: Privacy-Preserving distributed mining of association rules on horizontally partitioned data. IEEE Trans. on Knowledge and Data Engineering 16(9), 1026–1037 (2004)
Warmer, S.L.: Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association 60(309), 63–69 (1965)
Evfimievski, A.: Randomization in privacy preserving data mining. ACM SIGKDD Explorations Newsletter 4(2), 43–48 (2002)
Vaidya, J., Clifton, C.: Privacy preserving association rule mining in vertically partitioned data. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 45(4), pp. 57–62. ACM Press, New York (2002)
Yen, S.J., Chen, A.L.P.: A graph-based approach for discovering various types of association rules. IEEE Transactions on Knowledge and Data Engineering 13(5), 839–845 (2001)
Breslow, L.A., Aha, D.W.: Simplifying decision trees: A survey. The Knowledge Engineering Review 12(1), 31–40 (1997)
Shang, W.: Statistical Analysis Frame of Complex Data Mining and MART & MCMC Applications in CRM. Sichuan University, Chongqing, Sichuan 34(5), 56–62 (2004)
Zhang, H.: Data Mining Study Based on Rough Sets and Fuzzy Neural Network. Tianjin University, Tianjin 8(2), 75–83 (2006)
Wang, W.: Design and Realization of Long-Distance Education Based on Web. China University of Geosciences, Beijing 63(4), 766–771 (2008)
Zhang, S.: The system design and data mining of education website based on Web technology. Shandong University, Jinan, Shandong 54(8), 65–69 (2005)
Stephens, R., Plew, R.R., He, Y., et al.: Design of database, vol. 55(6) pp. 3–14. Mechanical Industry Press, Beijing (2001)
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Yang-bo, W. (2013). Web Database Based on Data Mining. In: Yang, Y., Ma, M., Liu, B. (eds) Information Computing and Applications. ICICA 2013. Communications in Computer and Information Science, vol 392. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53703-5_9
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DOI: https://doi.org/10.1007/978-3-642-53703-5_9
Publisher Name: Springer, Berlin, Heidelberg
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