Gaussian mixture models are a very useful tool for modeling data distribution. While estimating parameters using the expectation-maximization algorithm, this approach does not scale well with big datasets, especially if it is necessary to prepare many models for the proper selection of metaparameters. In this article we present an approximation of the expectation-maximization algorithm obtained by merging crucial subsets of the dataset, that differ slightly in their effect on the expectation-maximization loss function, into information granules. Furthermore, application examples comparing new method with the classical approach are shown.