This article presents a clustering-based approach to fuzzy system identification. In order to construct an effective initial fuzzy model, this article tries to present a modular method to identify fuzzy systems based on a hybrid clustering-based technique. Moreover, the determination of the proper number of clusters and the appropriate location of clusters are one of primary considerations on constructing an effective initial fuzzy model. Due to the above reasons, a hybrid clustering algorithm concerning input, output, generalization and specialization has hence been introduced in this article. Further, the proposed clustering technique, three-part input-output clustering algorithm, integrates a variety of clustering features simultaneously, including the advantages of input clustering, output clustering, flat clustering, and hierarchical clustering, to effectively perform the identification of clustering problem.