Membrane computing, known as P systems or membrane systems, is a novel class of distributed and parallel computing models. This paper proposes a membrane clustering algorithm using hybrid evolutionary mechanisms to address data clustering problem. It uses a tissue P system consisting of three cells to find the optimal cluster centers for a data set to be clustered. Its object is used to express candidate cluster centers, and the three cells use three different evolutionary mechanisms: genetic operators, velocity-position model and differential evolution mechanism. Particularly, the velocity-position model and differential evolution mechanism used in the process are the improved versions proposed in this paper according to the special membrane structure and communication mechanism. The hybrid evolutionary mechanisms can enhance the diversity of objects in the system and improve the convergence performance. Under the control of the hybrid evolutionary mechanisms and communication mechanism, the membrane clustering algorithm can determine a good partition for a data set. The proposed membrane clustering algorithm is evaluated on three artificial data sets and five real-life data sets and compared with k-means and several evolutionary clustering algorithms. Statistical significance tests have been performed to establish the superiority of the proposed membrane clustering algorithm.