As a strategic move toward improving the utility of computer- aided diagnosis (CAD) in breast cancer detection, this work aims to develop a computer-based decision support system, through a visual mapping of featured database, to explain the entire decision making process jointly by the computer-encoded knowledge and the user-interaction. The main purpose of the work is twofold: enhance the clinical utility of CAD and provide a mechanism for optimal system design. We adopt a mathematical feature extraction procedure to construct the featured database from the suspicious mass sites localized by the enhanced segmentation. The optimal mapping of the data points is then obtained by learning a hierarchical normal mixtures and associated decision boundaries. A visual explanation of the decision making is further invented through a multivariate data mining and knowledge discovery scheme. In particular, using multiple finite normal mixture models and hierarchical visualization spaces, new strategy is that the top-level model and projection should explain the entire data set, best revealing the presence of clusters and relationships, while lower-level models and projections should display internal structure within individual clusters, such as the presence of subclusters, which might not be apparent in the higher-level models and projections. We demonstrate the principle of the approach on several multimodal numerical data sets, and we then apply the method to the visual explanation in CAD for breast cancer detection from digital mammograms.