Association rule mining aims to discover sets of features that occur together. A variation of association rule min- ing is ratio rule mining. A ratio rule is an eigenvector of the database that describes ratios of features. However, ra- tio rules are sensitive to outliers. In this work, we design a prevalence-based model for mining ratio patterns from a database. Our model is more robust to noises, and ratio patterns in our model have clear statistic meanings. We develop an algorithm to quickly determine the sets of fea- tures and their ratios that satisfy the prevalence require- ment. Data structures, such as hash table and hash tree are utilized to further improve the efficiency of the algorithm. Experiments on synthetic data indicates the efficiency and scalability of the proposed algorithm. We also present a case study on US census data.