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Recently Web mining has become a hot research topic, which combines two of the prominent research areas comprising of data mining and the World Wide Web (WWW). 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, business and support services, personalization, network traffic flow analysis and so on. Our previous study on Web usage mining using a concurrent neuro-fuzzy approach has shown that the usage trend analysis very much depends on the performance of the clustering of the number of requests. In this paper, a novel approach 'intelligent-miner' (i-Miner) is introduced to optimize the concurrent architecture of a fuzzy clustering algorithm (to discover data clusters) and a fuzzy inference system to analyze the trends. In the concurrent neuro-fuzzy approach, self-organizing maps were used to cluster the Web user requests. A hybrid evolutionary FCM approach 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. Empirical results clearly shows that the proposed technique is efficient with lesser number of if-then rules and improved accuracy at the expense of complicated algorithms and extra computational cost.