While the centroid-less approach in FKMWC offers advantages in terms of flexibility and robustness, there are potential limitations compared to traditional Fuzzy K-Means Clustering. One drawback is the increased computational complexity of the algorithm, as it requires the direct optimization of the membership matrix based on the distance matrix. This can lead to longer processing times, especially for large datasets. Additionally, the centroid-less approach may struggle with certain types of data distributions, particularly those that do not have clear cluster boundaries. Traditional Fuzzy K-Means Clustering with cluster centroids may be more effective in such cases as it explicitly defines cluster centers for data points to gravitate towards, providing a clearer partitioning of the data.