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
efficient.
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