This paper presents a general framework for adapting any generative (model-based) clustering algorithm to provide balanced solutions, i.e., clusters of comparable sizes. Partitional, model-based clustering algorithms are viewed as an iterative two-step optimization process---iterative model re-estimation and sample re-assignment. Instead of a maximum-likelihood (ML) assignment, a balance-constrained approach is used for the sample assignment step. An efficient iterative bipartitioning heuristic is developed to reduce the computational complexity of this step %to $O(kn(k+\log{n})$ and make the balanced sample assignment algorithm scalable to large datasets. We demonstrate the superiority of this approach to regular ML clustering on complex data such as arbitrary-shape 2-D spatial data, high-dimensional text documents, and EEG time series.