In order to overcome the shortcoming that traditional classifiers cannot achieve satisfactory generalization performance, good interpretability and fast learning efficiency for datasets, the zero-order TSK fuzzy classifier called TSK-FC is proposed to solve the classification problem of middle-scale datasets.In order to make the TSK-FC suitable for large-scale data sets, its incremental version called TSK-IFC is developed, in which the incremental fuzzy clustering algorithm called incremental fuzzy ($c+p$)-means clustering(IFCM($c+p$)) is used to train antecedent parameters of fuzzy rules while fast consequent parameter learning is achieved through an appropriate matrix computation trick for the least learning machine.The proposed fuzzy classifiers, the TSK-FC and the TSK-IFC are experimentally compared with the conventional fuzzy classifier called FCPM-IRLS and the RBF neural network, and the results show the power of the proposed fuzzy classifiers, especially the great applicability of the TSK-IFC for large-scale data sets.