%0 Conference Paper %1 abraham:2003:CEC %A Abraham, Ajith %A Ramos, Vitorino %B Proceedings of the 2003 Congress on Evolutionary Computation CEC2003 %C Canberra %D 2003 %E Sarker, Ruhul %E Reynolds, Robert %E Abbass, Hussein %E Tan, Kay Chen %E McKay, Bob %E Essam, Daryl %E Gedeon, Tom %I IEEE Press %K ant artificial mining usage web %P 1384--1391 %T Web Usage Mining Using Artificial Ant Colony Clustering and Genetic Programming %U http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-CEC03b.pdf %X The rapid e-commerce growth has made both business community and customers face a new situation. Due to intense competition on one hand and the customer's option to choose from several alternatives business community has realized the necessity of intelligent marketing strategies and relationship management. Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis and so on. The study of ant colonies behavior and their self-organizing capabilities is of interest to knowledge retrieval/management and decision support systems sciences, because it provides models of distributed adaptive organization, which are useful to solve difficult optimization, classification, and distributed control problems, among others. In this paper, we propose an ant clustering algorithm to discover Web usage patterns (data clusters) and a linear genetic programming approach to analyze the visitor trends. Empirical results clearly shows that ant colony clustering performs well when compared to a self-organizing map (for clustering Web usage patterns) even though the performance accuracy is not that efficient when comparared to evolutionary-fuzzy clustering (i-miner) approach. %@ 0-7803-7804-0

@inproceedings{abraham:2003:CEC, abstract = {The rapid e-commerce growth has made both business community and customers face a new situation. Due to intense competition on one hand and the customer's option to choose from several alternatives business community has realized the necessity of intelligent marketing strategies and relationship management. Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis and so on. The study of ant colonies behavior and their self-organizing capabilities is of interest to knowledge retrieval/management and decision support systems sciences, because it provides models of distributed adaptive organization, which are useful to solve difficult optimization, classification, and distributed control problems, among others. In this paper, we propose an ant clustering algorithm to discover Web usage patterns (data clusters) and a linear genetic programming approach to analyze the visitor trends. Empirical results clearly shows that ant colony clustering performs well when compared to a self-organizing map (for clustering Web usage patterns) even though the performance accuracy is not that efficient when comparared to evolutionary-fuzzy clustering (i-miner) approach.}, added-at = {2009-05-09T21:41:28.000+0200}, address = {Canberra}, author = {Abraham, Ajith and Ramos, Vitorino}, biburl = {https://www.bibsonomy.org/bibtex/29a9643fb49b557cf4d92f8bc751a04d0/philipp.fischer}, booktitle = {Proceedings of the 2003 Congress on Evolutionary Computation CEC2003}, editor = {Sarker, Ruhul and Reynolds, Robert and Abbass, Hussein and Tan, Kay Chen and McKay, Bob and Essam, Daryl and Gedeon, Tom}, interhash = {40ebf4ce04f6fec7c4fbf2d1a424b75b}, intrahash = {9a9643fb49b557cf4d92f8bc751a04d0}, isbn = {0-7803-7804-0}, keywords = {ant artificial mining usage web}, month = {8-12 December}, notes = {CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.}, organisation = {IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)}, pages = {1384--1391}, publisher = {IEEE Press}, publisher_address = {445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA}, size = {8 pages}, timestamp = {2009-05-09T21:41:28.000+0200}, title = {Web Usage Mining Using Artificial Ant Colony Clustering and Genetic Programming}, url = {http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-CEC03b.pdf}, year = 2003 }