This paper describes an algorithm for a direct solution of domain adaptation (DA) to transform data in source domain to match the distribution in the target domain. This is achieved by formulating a transformation matrix based on the Geometric Mean of Co-Variances (GMCV), estimated from the covariance matrices of the data from both the domains. As a pre-processing step, we propose an iterative framework for clustering over data from both the domains, to produce an inter-domain mapping function of clusters. A closed form solution for direct DA is obtained from the GMCV formulation. Experimental results on real world datasets confirms the importance of clustering prior to transformation using GMCV for better classification accuracy. Results show the superior result of the proposed method of DA, when compared with a few state of the art methods. © Springer-Verlag 2013.