Web Usage Mining Using Artificial Ant Colony Clustering and Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  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 Ramos