The well-known unsupervised classification algorithm called 'k-means' requires iterative and repetitive access to the data, which goes to the same (detailed) data many times over. These repeated calculations can prove costly especially when it comes to classifying massive data. In this article we propose to extend the k-means algorithm by introducing an optimization approach based on the dynamic pre-calculation of aggregates that can then be reused to avoid redundant calculations.