The visualization process usually only focuses on the results of the current visualization, losing the review and analysis of historical information. It implies that some important intermediate results are not tracked and detected timely, and often makes the information and the potential law invisible. The university graduate student information data is used as an example. When faculty in university visualize the students' personal information, they usually focus on the current view, ignoring the trace of the historical views, which contains important information or patterns. To address this problem and figure out the unknowns hidden in educational datasets, this paper proposes the historical views navigation through a similarity and closeness centrality based recommendation. In this approach, the useful intermediate views, or the interested views of the users are saved as history and compared with the current view. By analyzing the similarities between the closeness centrality and the path centrality, the most relative historical views are recommended to the user. Finally, the user study shows that most of the participants believe that this approach is a necessary alternative to properly integrate the current view and the historical views, which enhances user experience significantly.