Unsupervised learning based clustering methods are gaining importance in the field of data analytics, owing to the features they possess, such as high accuracy, simple implementation and fast computation, when compared with conventional supervised learning methods. Among several types of clustering techniques, those implying optimization routines are found to be more efficient. However, explosion in number of decision variables is making these algorithms computationally intensive. The authors present an efficient two-stage optimization based fuzzy clustering formulation, which works through variable reduction approach. The membership values associated with each data point, forming the majority of decision variables, are estimated under an artificial neural networks framework. The reduction in decision variables allows the implementation of evolutionary optimization solvers to solve the single objective constrained optimization problem of fuzzy clustering increasing the chance of finding global optima. Additionally, this formulation estimates the optimal network topology and optimal number of clusters, which is not estimated rather assumed by other formulations. The proposed algorithm has been implemented on three different test data sets and the efficacy of the novel approach has been demonstrated by comparing the obtained clustering results with that of conventional fuzzy clustering approach. © 2019 IEEE.