Business Intelligence from Web Usage Mining

Title: Business Intelligence from Web Usage Mining

Research Question: How can Web usage mining be used to enhance business intelligence and improve customer experience in e-commerce?

Methodology: The study used a combination of data collection, clustering, and analysis techniques to extract valuable insights from web usage data. The authors proposed a novel approach called 'intelligent-miner' (i-Miner) that optimizes the concurrent architecture of a fuzzy clustering algorithm and a fuzzy inference system to analyze web site visitor trends. They also presented a hybrid evolutionary fuzzy clustering algorithm to optimally segregate similar user interests. The clustered data was then used to analyze trends using a Takagi-Sugeno fuzzy inference system learned using a combination of evolutionary algorithm and neural network learning.

Results: The results showed that the proposed Web usage-mining framework was efficient in providing valuable insights into user behavior and preferences. The approach was compared with other techniques like self-organizing maps and various function approximation techniques, and the results were graphically illustrated.

Implications: The research has significant implications for businesses operating in the e-commerce space. By analyzing web usage data, businesses can gain a deeper understanding of their customers' behavior, preferences, and needs. This can help them to develop more effective marketing strategies, personalize user experiences, and improve overall customer satisfaction. Furthermore, the techniques presented in the study can be applied to a variety of other fields, such as social network analysis, healthcare, and education, where understanding user behavior and preferences is crucial for success.

Link to Article: https://arxiv.org/abs/0405030v1 Authors: arXiv ID: 0405030v1