Although OLAP (on-line analytical processing) provides various kinds of explorational and analytical functions, the analysts may ignore important information based on hypothesis-driven exploration in a large search space. Moreover, the existing discovery-driven exploration is based on exceptional cells which can be easily affected by noise. Cube navigation is an effective method which can induce an analyst to the most surprising parts of the cube. To overcome this problem, a new navigation method is proposed, which regards dimensions and dimensional members as skeleton of the data cube. Through extracting the distribution feature of the corresponding data set, the dimensions and their members are assigned proper surprising values as the navigation light. Experiments prove that the method is practical and effective.