Model for election night forecasting applied to the 2004 South African elections

Greben, JMElphinstone, EHolloway, Jennifer P2007-10-182007-10-182006-06Greben. JM, Elphinstone, E and Holloway, J. 2006. Model for election night forecasting applied to the 2004 South African elections. Orion: The Journal of ORSSA, Vol. 22(1), pp 1-220529-191Xhttp://hdl.handle.net/10204/1342Copyright: 2006 ORSSAA 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 the authors explain the methodology used to obtain the predictions. In particular, they look at the different clustering techniques that can be used, such as k-means, fuzzy clustering and k-means in combination with discriminate analysis. The authors assess the power of the different approaches by comparing their convergence towards the final results.enClusteringForecastingElectionsModel for election night forecasting applied to the 2004 South African electionsArticleGreben, J., Elphinstone, E., & Holloway, J. P. (2006). Model for election night forecasting applied to the 2004 South African elections. http://hdl.handle.net/10204/1342Greben, JM, E Elphinstone, and Jennifer P Holloway "Model for election night forecasting applied to the 2004 South African elections." (2006) http://hdl.handle.net/10204/1342Greben J, Elphinstone E, Holloway JP. Model for election night forecasting applied to the 2004 South African elections. 2006; http://hdl.handle.net/10204/1342.TY - Article AU - Greben, JM AU - Elphinstone, E AU - Holloway, Jennifer P AB - 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 the authors explain the methodology used to obtain the predictions. In particular, they look at the different clustering techniques that can be used, such as k-means, fuzzy clustering and k-means in combination with discriminate analysis. The authors assess the power of the different approaches by comparing their convergence towards the final results. DA - 2006-06 DB - ResearchSpace DP - CSIR KW - Clustering KW - Forecasting KW - Elections LK - https://researchspace.csir.co.za PY - 2006 SM - 0529-191X T1 - Model for election night forecasting applied to the 2004 South African elections TI - Model for election night forecasting applied to the 2004 South African elections UR - http://hdl.handle.net/10204/1342 ER -