The FKMWC framework can be adapted to incorporate semi-supervised or constrained clustering scenarios by integrating additional constraints or information into the optimization process. For semi-supervised clustering, labeled data points can be incorporated into the objective function to guide the clustering process towards known labels. This can be achieved by modifying the objective function to include terms that penalize deviations from the labeled data points. Similarly, for constrained clustering scenarios where certain data points are required to belong to specific clusters, constraints can be added to the optimization process to enforce these relationships. By incorporating such constraints, the FKMWC framework can be tailored to handle semi-supervised or constrained clustering scenarios effectively.