Business Intelligence from Web Usage Mining

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Ajith Abraham",
  title =        "Business Intelligence from Web Usage Mining",
  journal =      "Journal of Information \& Knowledge Management",
  year =         "2003",
  volume =       "2",
  number =       "4",
  pages =        "375--390",
  keywords =     "genetic algorithms, genetic programming, Web mining,
                 knowledge discovery, business intelligence, hybrid soft
                 computing, neuro-fuzzy-genetic system",
  URL =          "",
  DOI =          "doi:10.1142/S0219649203000565",
  size =         "16 pages",
  abstract =     "The rapid e-commerce growth has made both business
                 community and customers face a new situation. Due to
                 intense competition on the one hand and the customer's
                 option to choose from several alternatives, the
                 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. This paper presents the important
                 concepts of Web usage mining and its various practical
                 applications. Further a novel approach called
                 {"}intelligent-miner{"} (i-Miner) is presented. i-Miner
                 could optimize the concurrent architecture of a fuzzy
                 clustering algorithm (to discover web data clusters)
                 and a fuzzy inference system to analyze the Web site
                 visitor trends. A hybrid evolutionary fuzzy clustering
                 algorithm is proposed to optimally segregate similar
                 user interests. The clustered data is then used to
                 analyze the trends using a Takagi-Sugeno fuzzy
                 inference system learned using a combination of
                 evolutionary algorithm and neural network learning.
                 Proposed approach is compared with self-organizing maps
                 (to discover patterns) and several function
                 approximation techniques like neural networks, linear
                 genetic programming and Takagi?Sugeno fuzzy inference
                 system (to analyze the clusters). The results are
                 graphically illustrated and the practical significance
                 is discussed in detail. Empirical results clearly show
                 that the proposed Web usage-mining framework is
  notes =        "see also


        Department of Computer
                 Science, Oklahoma State University, 700 N Greenwood
                 Avenue, Tulsa, Oklahoma 74106-0700, USA",

Genetic Programming entries for Ajith Abraham