Short-term electricity price forecasting has now become crucial in deregulated electricity markets, as it forms the basis for maximising the profits of the market participants. In this paper, a hybrid forecasting model that combines a Fuzzy-C-Means clustering technique with a Support Vector Machine (SVM) algorithm is proposed to forecast the half-hour electricity prices in UK electricity markets. In order to demonstrate the predictive ability of the proposed model, Artificial Neural Networks and SVM models are also presented and compared with the hybrid model based on the same training and testing data sets. The publicly available data was obtained from APX Power UK for the year 2007. Two case studies are presented with forecasts from the three predictors. Mean Absolute Percentage Error is used to analyze the forecasting errors of different models and the results presented clearly show that the proposed hybrid model considerably improves the forecasting.