Web Usage Mining Using Clustering Algorithms (Case Study of LAUTECH Students)

This study explores the application of Web Usage Mining through clustering algorithms to analyze student web interactions at Ladoke Akintola University of Technology (LAUTECH), Ogbomoso, Nigeria. Leveraging server log data collected over four weeks, the research implements K-means, DBSCAN, Agglomerative Clustering, and Self-Organizing Maps (SOM) to identify user behavior patterns, including login frequencies, session durations, and platform preferences. Preprocessing steps such as log parsing, noise removal, and session identification were critical to structuring raw data for analysis. Results revealed three distinct user clusters (frequent, moderate, and infrequent users) via K-means and Agglomerative methods, while DBSCAN highlighted noise (16 outliers) and SOM provided granular spatial insights. The Key findings include Android dominance (70–80% of users) across clusters, K-means achieved the most actionable segmentation (Silhouette Score: 0.43); and DBSCAN excelled in noise detection (Silhouette Score: 0.86) but failed to form clusters. The study demonstrates web usage mining’s potential to optimize institutional web services and personalize user experiences. Challenges like data sparsity and computational complexity are noted, with recommendations for future work on real-time analytics and cross-institutional comparisons.