A novel model has been developed to predict elections on the basis of early results. The electorate is clustered according to their behaviour in previous elections. Early results in the new elections can then be translated into voter behaviour per cluster and extrapolated over the whole electorate. This procedure is of particular value in the South African elections which tend to be highly biased, as early results do not give a proper representation of the overall electorate. In this paper we explain the methodology used to obtain the predictions. In particular, we look at the different clustering techniques that can be used, such as kmeans, fuzzy clustering and k-means in combination with discriminant analysis. We assess the performances of the different approaches by comparing their convergence towards the final results.