Precise segmentation of tumor regions plays prominent role in the grading of breast carcinoma using the Nottingham Histological Grading (NHG) system. A robust segmentation framework is expected to produce cost-effective, repeatable, and reproducible quantitative outputs. In this study, a spatial neighborhood intensity constraint (SNIC) clustering framework for tumor region in breast histopathology images is presented. The proposed framework consists of five main stages: (1) color normalization, (2) segmentation and removal of nucleus cells, (3) SNIC, (4) FCM with knowledge-based initial centroids selection, and (5) post-processing. The novelty of the proposed framework lies within its simple but powerful in clustering tumor regions precisely in a heterogenous environment. The SNIC is implemented to remove and replace the intensity of the nucleus cells based on the spatial constraints. Also, a knowledge-based initial centroids selection method is implemented to ease the FCM clustering algorithm. Both of these methods are posited to facilitate the clustering stage producing complementary results. To validate the hypothesis, careful justifications are performed to evaluate the role of SNIC and knowledge-based initial centroids selection. These methods are found plausible by achieving positive results in \(Acc\), \(F1\), \(AOM\), and \(CEI\) of 91.2%, 92.1%, 85.7%, and 90.1%, respectively. To further demonstrate the applicability of the proposed framework, four recent works are included for benchmarking purposes. The proposed framework found outperformed these methods with the lowest percentages in over-segmentation and under-segmentation: 8.7% and 6.6%, respectively.