Abstract: Cluster analysis is an important tool of unsupervised pattern recognition. It has been used in diverse fields such as data mining, biology, computer vision, and document analysis. The fuzziness index m has important influence on the clustering result of fuzzy clustering algorithms and it should not be forced to fix at the usual value m=2. In view of its distinctive features in applications and its limitation of having m=2 only, a recent advance of fuzzy clustering called fuzzy c-means clustering with improved fuzzy partitions (IFP-FCM) is extended in this paper and a generalized algorithm called GIFP-FCM for more effective clustering is proposed. By introducing a novel membership constraint function, a new objective function is constructed and GIFP-FCM clustering is derived. Meanwhile, from the viewpoints of Voronoi distance and competitive learning, the robustness and convergence of the proposed algorithm are analyzed. The proposed GIFP-FCM algorithm not only inherits the merits of IFP-FCM, but also generalizes it so that the original limitation on the fuzziness index m can be removed. Furthermore, the classical fuzzy c-means algorithm (FCM) and IFP-FCM can be taken as two special cases of the proposed algorithm, and GIFP-FCM provides a reasonable link between FCM and IFP-FCM. Several experimental results including its application to noisy image texture segmentation demonstrate its average advantage over FCM and IFP-FCM in both clustering and robustness capability.