Business Intelligence from Web Usage Mining
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- @Misc{oai:arXiv.org:cs/0405030,
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title = "Business Intelligence from Web Usage Mining",
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author = "Ajith Abraham",
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year = "2004",
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month = may # "~06",
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keywords = "genetic algorithms, genetic programming",
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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. In this paper, we present the
important concepts of Web usage mining and its various
practical applications. We further present a novel
approach 'intelligent-miner' (i-Miner) to 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 in
this paper 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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identifier = "Journal of Information \& Knowledge Management (JIKM),
World Scientific Publishing Co., Singapore, Vol. 2, No.
4, pp. 375-390, 2003",
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oai = "oai:arXiv.org:cs/0405030",
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URL = "http://arXiv.org/abs/cs/0405030",
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notes = "see also \cite{Abraham:2003:JIKM}",
- }
Genetic Programming entries for
Ajith Abraham
Citations