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Forecasting price spikes is a timely issue for the deregulated electricity market. Traditional price forecasting techniques show poor performance in handling price spikes, which usually follow a pattern different from the prices under normal market conditions. Therefore, novel approaches are required to forecast both the occurrences and values of spikes. In this paper a comprehensive study is conducted to investigate the performance of several data mining techniques for spike forecasting. Another major contribution of this paper is that a novel approach is proposed to integrate the spike forecasting process with decision-making, and to provide a comprehensive risk management tool against spikes. This approach is based on the Naive Bayesian Classifier. The benefits/costs of possible decisions are considered in the spike forecasting process to achieve the maximum benefits from the decisions against price spikes. We give a comprehensive theoretical proof of the proposed Bayesian classifier with benefit maximisation (BCBM) approach, which empirically demonstrates its effectiveness by achieving promising experiment results on real market price datasets.