Navigating the vast expanse of the World Wide Web leaves a trail of data, but how can we decipher user behavior from millions of different navigation paths? This paper introduces a method for large-scale analysis of user clickstreams, transforming raw data into intuitive and insightful visualizations. The technique leverages concepts from data mining and graph layout optimization, providing a scalable solution for understanding web user activity. By applying association rules and computer graphics, the authors identify common clickpaths and present them in a visually compelling manner. This enables researchers and businesses to gain a clearer understanding of user navigation patterns, identifying popular content, and improving website design. The method's scalability and interpretability make it a valuable tool for web analytics, offering the potential to enhance user experience, optimize website structure, and improve online marketing strategies through data mining. The research is especially important given the current data driven enviroment and the increasing demand to understand consumer trends.
Published in the International Journal of Image and Graphics, this paper fits squarely within the journal's focus on visual computing and data representation. The work extends the journal's scope by presenting a novel method for visualizing web user clickpaths, effectively bridging the gap between data mining techniques and intuitive graphical representations of web activity. This contribution aligns with the journal's interest in innovative approaches to image and graphics processing.