Abstract: Breast cancer is the most prevailing type of cancer responsible for a large number of deaths every year. However, at the same time, this is largely a curable type of cancer if identified at initial stages. With major advances in research in the areas of image processing, data mining and clustering and machine learning, a more precise prognosis and prediction of breast cancer are possible at earlier stages. A fuzzy clustering model is a popular model used across various researches in image processing to predict the malignancy of breast tumor. The partitional clustering method finds its strength in its fuzzy partitioning such that a data point may belong to different classes with varying degrees of membership (ranging between 0 and 1), which is less rigid as compared to an older and still popular k-means clustering algorithm. The current article attempts to hybridize the fuzzy C-means with the cohort intelligence (CI) algorithm to optimize cluster formation. CI is a robust optimization metaheuristic belonging to the class of socio-inspired optimizers (Kumar M, Kulkarni A Socio-cultural inspired metaheuristics, pp 1–28, Springer International Publishing, 2019 ), motivated from self-adapting behavior of candidates in a cohort or a group. CI is typically characterized by its simple algorithmic nature, robust structure and a faster convergence rate, hence gaining popularity. This novel hybridized data clustering algorithm fuzzy-CI imitates the soft clustering and communal learning attitude of clusters and candidates. The hybridized method of fuzzy-CI is validated by testing it on the Breast Cancer Wisconsin (Diagnostic) Dataset. The results validate that the hybridized version exhibits better cluster formation in comparison with the non-hybridized version.