A simple and effective fuzzy clustering approach is presented for fuzzy modeling from industrial data. In this approach, fuzzy clustering is implemented in two phases: data compression by a self-organizing network, and fuzzy partitioning via fuzzy c-means clustering associated with a proposed cluster validity measure. The approach is used to extract fuzzy models from data and find out the optimal number of fuzzy rules. The simulation results show that the proposed approach has good clustering performance with noise-contaminated data and high-dimensional industrial data.