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 com- munity 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 eectiv e Web site management, creating adaptive Web sites, business and support services, personalization, network trac o w analy- sis and so on. This paper presents the important concepts of Web usage mining and its various practical applications. Fur- ther 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 clus- ters) and a fuzzy inference system to analyze the Web site visitor trends. A hybrid evolutionary fuzzy clustering algo- rithm is proposed to optimally segregate similar user inter- ests. 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 approxima- tion techniques like neural networks, linear genetic program- ming and Takagi{Sugeno fuzzy inference system (to analyze the clusters). The results are graphically illustrated and the practical signicance is discussed in detail. Empirical results clearly show that the proposed Web usage-mining framework is ecien t.
['Ajith Abraham']
Business Intelligence from Web Usage Mining