Generating adaptive network data visualization to different levels of users

Supervised learning algorithm is the machine learning task of inferring a function from supervised training data. We introduce a new network data visualization framework that operates with different supervised algorithms. This is because the existing network data visualization tools are mostly designed for network administrator or advanced user. The fancy interface and complicated visualization are only meaningful to network administrators and not to the beginner users. The purpose of this study is to reduce interface usability problems faced by network visualization users by creating tailored and skill-level specific visualizations based on real-time user feedback and machine learning algorithms. The proposed framework is also indirectly designed to assist in existing network data visualization implementation where the demand for visualizing different levels of network data details from different levels of computer users´ perspective has never been fulfilled. Experiment showed that the proposed framework managed to generate usable interface, perform better visualization and capable to adapt to the user feedback in the network data visualization, which preserving its capabilities of intelligently adjusting the network data visualization to different levels of computer users.