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Predicting the occurrence of solar flares is a challenge of great importancefor many space weather scientists and users. We introduce a data miningapproach, called Behavior Pattern Learning (BPL), for automaticallydiscovering correlations between solar flares and active region data, inorder to predict the former. The goal of BPL is to predict the interval oftime to the next solar flare and provide a confidence value for the associatedprediction. The discovered correlations are described in terms ofeasy-to-read rules. The results indicate that active region dynamics isessential for predicting solar flares

The influence of active region information on the prediction of solar flares: an empirical model using data mining