Summary: Fuzzy C-Means (FCM) and hard clustering are the most common tools for data partitioning. However, the presence of noisy observations in the data being partitioned may render these clustering algorithms unreliable. In this paper, we introduce a robust noise-rejection clustering algorithm based on a combination of techniques that treat the FCM pitfalls with an outliers exclusion criterion. Unlike the traditional FCM, the proposed clustering tool provides much efficient data partitioning capabilities in the presence of noise and outliers. At the conclusion of the theoretical development, we validate the effectiveness of the proposed noise-rejection data partitioning tool through various comparison studies with existing noise-rejection clustering approaches in the literature.