A new method for fast building the rough data model (RDM) by means of supervised fuzzy clustering in the product space of input and output variables is proposed. The approach incorporates the RDM’s classification quality performance index with Gustafson-Kessel (GK) clustering algorithm and is of many good properties. The way to convert the fuzzy cluster models to rough data models by introducing the concept of putative membership degree of a data point to a fuzzy cluster is suggested. Hence, an efficient algorithm that can obtain RDMs by just iteratively computing two necessary condition equations is worked out. It minimizes the objective function and turns the multi-dimensional search process of the Kowalczyk’s method to one dimensional search strategy (in terms of the number of clusters). This technique reduces the searching time greatly. Compared with the traditional rough set theory and the Kowalczyk’s method, the approach has more powerful ability to handle data contaminated by noise and better generalization ability. Finally, different examples of data sets illustrate the effectiveness of the approach.