In a visualization of a three-dimensional dataset, the insights gained are dependent on what is occluded and what is not. Suggestion of interesting viewpoints can improve both the speed and efficiency of data understanding. This paper presents a view selection method designed for volume rendering. It can be used to find informative views for a given scene, or to find a minimal set of representative views which capture the entire scene. It becomes particularly useful when the visualization process is non-interactive - for example, when visualizing large datasets or time-varying sequences. We introduce a viewpoint "goodness" measure based on the formulation of entropy from information theory. The measure takes into account the transfer function, the data distribution and the visibility of the voxels. Combined with viewpoint properties like view-likelihood and view-stability, this technique can be used as a guide which suggests "interesting" viewpoints for further exploration. Domain knowledge is incorporated into the algorithm via an importance transfer function or volume. This allows users to obtain view selection behaviors tailored to their specific situations. We generate a view space partitioning, and select one representative view for each partition. Together, this set of views encapsulates the "interesting" and distinct views of the data. Viewpoints in this set can be used as starting points for interactive exploration of the data, thus reducing the human effort in visualization. In non-interactive situations, such a set can be used as a representative visualization of the dataset from all directions.