Fuzzy modelling has interpretability of the obtained models as a fundamental goal. The approach sought for control-oriented local-model fuzzy clustering tries that those models approximate the linearized plant model on their validity zones. A family of clustering algorithms is presented so that it incorporates some desirable characteristics regarding convexity and smoothness of the final identified clusters, with advantages regarding other methodologies such as Gustaffson-Kessel. The algorithm simultaneously provided local linear models and input clustering, being specially suitable for Takagi-Sugeno models and local linear models decomposition of complex systems.