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[1] A. Luotonen, “The common logfile format,” 1995, http://www.w3.org/pub/WWW/ Daemon/User/Config/Logging.html. [2] P. M. Hallam-Baker, “Extended log file format.”, http://www.w3.org/pub/ WWW/TR/WD-logfile.html. [3] L. A. Zadeh, “The concept of a linguistic variable and its application to approximate reasoning,” Information Science, Vol. 9, pp.199-249, 1975. [4] F. R. McFadden and J. A. Hoffer, Database Management, Benjamin/Cummings, 1991. [5] P. O’Neil and E. O’Neil, Database: Principles, Programming, and Performance, Morgan Kaufmann, 1994. [6] J. P. Bigus, Data Mining with Neural Networks: solving business problems from application development to decision support, NY: McGraw-Hill,1996 [7] M. Umanol, H. Okamoto, I. Hatono, H. Tamura, F. Kawachi, S. Umedzu and J. Kinoshita, Fuzzy Decision Trees by Fuzzy ID3 Algorithm and Its Application to Diagnosis Systems, in Proc. of the 1994 Third IEEE Conf. on Fuzzy Systems,1994. [8] L. A. Zadeh, “Fuzzy sets,” Information Control, Vol. 8, pp. 338-353, 1965. [9] H. J. Zimmermann, Fuzzy Set Theory and Its Application, Boston, Dordrecht, Landon, 1991. [10] C. T. Lin and C. S. G. Lee, Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems, Upper Saddle River, New Jersey: Prentice-Hall, 1996. [11] G. A. Carpenter and S. Grossberg, “A massively parallel architecture for a self-organizing neural pattern recognition machine,” Comput. Vision Graphics Image Process, Vol. 37, pp. 54-115, 1987. [12] J. R. Quinlan, “Induction of Decision Trees,” Machine Learning, Vol. 1, pp. 81-106, 1986. [13] N. R. Pal, “Fuzzy Rule Extraction from ID3-Type Decision Trees for Real Data,” IEEE Trans on Systems, Vol. 31, No. 5, pp. 745-754, 2001. [14] H. Ichihashi, T. Shirai, K. Nagasaka, and T. Miyoshi, “Neural-fuzzy ID3: a method of inducing fuzzy decision trees with linear programming for maximizing entropy and an algebraic method for incremental learning,” Fuzzy Sets and Systems, Vol. 81, pp. 157-167, 1995. [15] J. Fong, J. G. Hughes, and J. Zhu, “Online web mining transactions association rules using frame metadata model,” in Proc. of the 2000 First Int. Conf. on Web Information Systems Engineering, Vol. 2, pp. 121-129, 2000. [16] I. Y. Lin, X. M. Huang, and M. S. Chen, “Capturing user Access patterns in the web for data mining,” in Proc. IEEE 11th Int. Conf. On Distributed Computing Systems, March 1996. [17] N. Megiddo and R. Srikant, “Discovering predictive association rules,” Proc. of the 4th Intl Conf. on knowledge Discovery in Databases and Data Mining, N. Y., August 1997. [18] O. R. Zaiane, M. Xin, and J. Han, “Dsicovering web access patterns and trends by applying OLAP and data mining technology on web logs,” in Proc. Advances Digital Libraries Conf., pp. 19-29, Santa Barbara, CA, April 1998. [19] C. Brunk, J. Kelly, and R. Kohavi., “An integrated system for data mining,” in Proc. of the 3rd Int. Conf. Knowledge Discovery and Data Mining, pp. 135-138, Newport Beach, CA, August 1997. [20] L. X. Wang, J. M. Mendel, “Generating fuzzy rules by learning from examples,” IEEE Trans. on Syst. Man Cybern, Vol. 22, No 6, pp. 1414-1427, 1992.
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