Fuzzy c-means (FCM) and hard clustering algorithms are the most common tools for data partitioning. However, these clustering algorithms may fail completely in the presence of noise. In this paper, we introduce a robust noise rejection clustering algorithm based on a combination of techniques that treat the FCM weak points with a traditional noise rejection algorithm. Unlike the traditional FCM, the proposed algorithm is a powerful tool for partitioning the data in the presence of noise (outliers).