Business Intelligence from Web Usage Mining
  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.
