Web Usage Mining Using Artificial Ant Colony Clustering and Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{abraham:2003:CEC,

• author = "Ajith Abraham and Vitorino Ramos",
• title = "Web Usage Mining Using Artificial Ant Colony Clustering and Genetic Programming",
• booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003",
• editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon",
• pages = "1384--1391",
• year = "2003",
• publisher = "IEEE Press",
• address = "Canberra",
• publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",
• month = "8-12 " # dec,
• organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)",
• keywords = "genetic algorithms, genetic programming, Web Usage Mining, Ant Systems, Stigmergy, Data-Mining, Linear Genetic Programming, Adaptive control, Ant colony optimization, Artificial intelligence, Communication system traffic control, Decision support systems, Knowledge management, Marketing management, Programmable control, Traffic control, Internet, artificial life, data mining, decision support systems, electronic commerce, self-organising feature maps, statistical analysis, Web site management, Web usage mining, artificial ant colony clustering algorithm, decision support systems, distributed adaptive organisation, distributed control problems, e-commerce, intelligent marketing strategies, knowledge discovery, knowledge retrieval, network traffic flow analysis, self-organizing map",
• ISBN = "0-7803-7804-0",
• URL = "http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-CEC03b.pdf",
• URL = "http://arxiv.org/abs/cs/0412071",
• DOI = "doi:10.1109/CEC.2003.1299832",
• size = "8 pages",
• 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.",
• notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.",

}

Genetic Programming entries for Ajith Abraham Vitorino J Castelo Ramos

Citations