Business Intelligence from Web Usage Mining

TL;DR

This paper introduces an innovative web usage mining framework combining fuzzy clustering and fuzzy inference systems, demonstrating improved efficiency in analyzing user behavior and trends for better web management and marketing strategies.

Contribution

The paper presents a novel hybrid evolutionary fuzzy clustering algorithm and a Takagi-Sugeno fuzzy inference system for web usage analysis, enhancing pattern discovery and trend analysis.

Findings

The proposed framework outperforms self-organizing maps in pattern discovery.

It achieves better trend analysis accuracy compared to neural networks and genetic programming.

Empirical results confirm the efficiency of the hybrid fuzzy approach.

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.