User interest based complex web information visualization

Web graph allows end user to visualize web information and its connectivity. But due to its huge size, it is very challenging to visualize web information from the enormous cyberspace. As a result, web graph lacks simplicity while the user is searching for information. Clustering and filtering techniques have been employed to make the visualization concise but it is often not accurate to the expectation of user because they do not utilize user-centric information. To deliver the information concisely and precisely to the end users according to their interest, we introduce personalized clustering techniques for web information visualization. We propose a system architecture, which considers the user interests while clustering & filtering, to make the web information more meaningful and useful to the end users. By the implementation of the architecture, an experimental example is provided to reflect our approach