The Intuitionistic Fuzzy C-means (IFCM) Clustering Algorithm is an extension of the Fuzzy C-means (FCM) Clustering Algorithm. Traditional research concerning the IFCM has predominantly concentrated on the integration of novel distance measures within the objective function. However, the weights in the objective function have consistently adhered to the constraints imposed by the original FCM algorithm. In this study, we propose a Bi-environmental Intuitionistic Fuzzy C-means (Bi-IFCM) Clustering Algorithm, wherein the weights are determined by employing intuitionistic fuzzy logic. The effectiveness of the Bi-IFCM algorithm is validated by its application to four UCI datasets within clustering tasks, demonstrating its superiority relative to previously modified IFCM algorithms. Furthermore, in the image segmentation task using the Berkeley Segmentation Dataset and Benchmark 500 (BSDS500), Bi-IFCM exhibited enhanced performance over preceding IFCM algorithms, as evidenced by improved dice similarity coefficient, accuracy, precision, and recall metrics.