BUSINESS INTELLIGENCE FROM WEB USAGE MINING

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 ‘intelligentminer’

(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.

...