With the explosive growth of data available on the
Internet, personalization of this information space become a
necessity. At present time with the rapid increasing popularity of the
WWW, Websites are playing a crucial role to convey knowledge and
information to the end users. Discovering hidden and meaningful
information about Web users usage patterns is critical to determine
effective marketing strategies to optimize the Web server usage for
accommodating future growth. The task of mining useful information
becomes more challenging when the Web traffic volume is enormous
and keeps on growing. In this paper, we propose a intelligent model
to discover and analyze useful knowledge from the available Web
log data.
[1] X. Wanga, A. Abraham, K. A. Smitha. Intelligent web traffic mining and
analysis. Journal of Network and Computer Applications, vol. 28, 2004,
pp. 147-165.
[2] P. Batista, M. J. Silva, "Mining web access logs of an on-line
newspaper," (2002), http://www.ectrl.itc.it/rpec/RPEC-Papers/11-
batista.pdf.
[3] R. Cooley, B. Mobasher, and J. Srivastava, "Web mining: Information
and pattern discovery on the World Wide Web," Proc. 9th IEEE Int.
Conf. Tools with Artificial Intelligence, Nov. 1997, pp. 558-567.
[4] R. Kosala, H. Blockeel, Web Mining Research: A Survey, SIGKKD
Explorations, vol. 2(1), July 2000.
[5] M. Baglioni, U. Ferrara, A. Romei1, S. Ruggieri, and F. Turini,
Preprocessing and Mining Web Log Data for Web Personalization.
(2003), http://www.di.unipi.it/~ruggieri/Papers/aiia2003.pdf.
[6] R. Iváncsy, I. Vajk, Different Aspects of Web Log Mining. 6th
International Symposium of Hungarian Researchers on Computational
Intelligence. Budapest, Nov., 2005.
[7] J. Everts and M. Bulacu, Assignment: Clustering of Web Users.
Groningen University, Netherlands, Nov. 22, (2005)
http://www.ai.rug.nl/ki2/assignments/ki2-assig03.pdf.
[8] J. Srivastava, R. Cooley, M. Deshpande, P.-N. Tan, Web Usage Mining:
Discovery and Applications of Usage Patterns from Web Data, SIGKKD
Explorations, vol.1, Jan 2000.
[9] B. Mobasher, H. Dai, T. Luo, N. Nakagawa, Y. Sun, J. Wiltshire,
Discovery of Aggregate Usage Profiles for Web Personalization, Proc.
of the Web Mining for E-Commerce Workshop (WebKDD-2000),
August 2000.
[10] B. Moshaber, R. Cooley, J. Srivastava, Automatic Personalization Based
on Web Usage Mining, Communications of the ACM, vol.43(8), 2000.
[11] A. Abraham. Business Intelligence from Web Usage Mining. Journal of
Information & Knowledge Management, Vol. 2, 2003, pp. 375-390
[12] S. K. Pal, V Talwar, P Mitra. Web Mining in Soft Computing
Framework: Relevance. State of the Art and Future Directions. IEEE
Trans. on Neural Networks, vol.13 (5), 2002, pp. 1163-77.
[1] X. Wanga, A. Abraham, K. A. Smitha. Intelligent web traffic mining and
analysis. Journal of Network and Computer Applications, vol. 28, 2004,
pp. 147-165.
[2] P. Batista, M. J. Silva, "Mining web access logs of an on-line
newspaper," (2002), http://www.ectrl.itc.it/rpec/RPEC-Papers/11-
batista.pdf.
[3] R. Cooley, B. Mobasher, and J. Srivastava, "Web mining: Information
and pattern discovery on the World Wide Web," Proc. 9th IEEE Int.
Conf. Tools with Artificial Intelligence, Nov. 1997, pp. 558-567.
[4] R. Kosala, H. Blockeel, Web Mining Research: A Survey, SIGKKD
Explorations, vol. 2(1), July 2000.
[5] M. Baglioni, U. Ferrara, A. Romei1, S. Ruggieri, and F. Turini,
Preprocessing and Mining Web Log Data for Web Personalization.
(2003), http://www.di.unipi.it/~ruggieri/Papers/aiia2003.pdf.
[6] R. Iváncsy, I. Vajk, Different Aspects of Web Log Mining. 6th
International Symposium of Hungarian Researchers on Computational
Intelligence. Budapest, Nov., 2005.
[7] J. Everts and M. Bulacu, Assignment: Clustering of Web Users.
Groningen University, Netherlands, Nov. 22, (2005)
http://www.ai.rug.nl/ki2/assignments/ki2-assig03.pdf.
[8] J. Srivastava, R. Cooley, M. Deshpande, P.-N. Tan, Web Usage Mining:
Discovery and Applications of Usage Patterns from Web Data, SIGKKD
Explorations, vol.1, Jan 2000.
[9] B. Mobasher, H. Dai, T. Luo, N. Nakagawa, Y. Sun, J. Wiltshire,
Discovery of Aggregate Usage Profiles for Web Personalization, Proc.
of the Web Mining for E-Commerce Workshop (WebKDD-2000),
August 2000.
[10] B. Moshaber, R. Cooley, J. Srivastava, Automatic Personalization Based
on Web Usage Mining, Communications of the ACM, vol.43(8), 2000.
[11] A. Abraham. Business Intelligence from Web Usage Mining. Journal of
Information & Knowledge Management, Vol. 2, 2003, pp. 375-390
[12] S. K. Pal, V Talwar, P Mitra. Web Mining in Soft Computing
Framework: Relevance. State of the Art and Future Directions. IEEE
Trans. on Neural Networks, vol.13 (5), 2002, pp. 1163-77.
@article{"International Journal of Business, Human and Social Sciences:60251", author = "Farhad F. Yusifov", title = "Web Traffic Mining using Neural Networks", abstract = "With the explosive growth of data available on the
Internet, personalization of this information space become a
necessity. At present time with the rapid increasing popularity of the
WWW, Websites are playing a crucial role to convey knowledge and
information to the end users. Discovering hidden and meaningful
information about Web users usage patterns is critical to determine
effective marketing strategies to optimize the Web server usage for
accommodating future growth. The task of mining useful information
becomes more challenging when the Web traffic volume is enormous
and keeps on growing. In this paper, we propose a intelligent model
to discover and analyze useful knowledge from the available Web
log data.", keywords = "Clustering, Self organizing map, Web log files, Web
traffic.", volume = "2", number = "9", pages = "1021-3", }