Vector quantization of large datasets can be carried out by means of an incremental modelling approach where the modelling task is transformed into an incremental task by partitioning or sampling the data, and the resulting datasets are processed by means of an incremental learner. Growing Neural Gas is an incremental vector quantization algorithm with the capabilities of topology-preserving and distribution-matching. Distribution matching can produce overpopulation of prototypes in zones with high density of data. In order to tackle this drawback, we introduce some modifications to the original Growing Neural Gas algorithm by adding three new parameters, one of them controlling the distribution of the codebook and the other two controlling the quantization error and the amount of units in the network. The resulting learning algorithm is capable of efficiently quantizing large datasets presenting high and low density regions while solving the prototype proliferation problem.