WEB SERVER CLIENT COMMUNICATION FOR WEB USAGE DATA ANALYSIS USING K-MEANS CLUSTERING

Information from web server logs can be useful to know about user interaction such as page requests, how often users visit a website, and how long users stay at a website. The actual usage of Web pages, though, can include redundant, incomplete or otherwise irrelevant records that do not lend themselves to direct analysis. This paper introduces a web usage data analysis model which uses the web server-client interaction, data preprocessing, feature extraction and K-Means clustering to find different user behaviour patterns. The suggested workflow involves web usage log collection and preprocessing, followed by the extraction of the behavioral features of web usage (e.g., number of visited pages and web usage time). The experimental analysis was done using a dataset of 15 users, clustered with the K-Means algorithm using 3 clusters. The data was processed, clustered, and visualized using Python, Pandas, NumPy, Matplotlib, and Scikit-learn. These clusters correspond to relatively low, moderate and high activity with the centres of the clusters being 8.4 page visits and 3.8 min, 17.8 page visits and 8.8 min, and 28.4 page visits and 14.0 min, respectively. The outcomes show the feasibility of using K-Means to cluster web users based on their simple usage properties and serve as a starting point for the next step of mining the web usage and analysis of the user behavior.