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In this work, a novel entropy-weighted fuzzy c-means variation is proposed. This varia-tion introduces a semantic level of partitioning of features into groups. This approach en-ables the provision of optimal semantic meaning to the clusters, thereby capturing the in-trinsic structure of the features, which are naturally grouped into homogeneous semantic sets. Additionally, it is computationally more efficient than other cluster-specific weighted fuzzy clustering algorithms, due to the independence of the weights from the clusters. The efficacy of the method was assessed by evaluating census data from 16 Italian cities, with the objective of partitioning urban settlements based on characteristics of residential buildings, including construction technique, period, number of floors, and state of con-servation. The findings suggest that the proposed algorithm effectively captures the se-mantic meaning of clusters. A comparative analysis of the two algorithms reveals that the new algorithm offers similar outcomes to the traditional EWFCM algorithm while signifi-cantly enhancing computational speed.