Abstract
In this paper, we investigate interpolation methods that are suitable for discovering spatiotemporal association rules for unsampled sites with a focus on drought risk management problem. For drought risk management, raw weather data is collected, converted to various indices, and then mined for association rules. To generate association rules for the unsampled sites, interpolation methods can be applied at any stage of this data mining process. We develop and integrate three interpolation models into our association rule mining algorithm. We call them pre-order, in-order and post-order interpolation models. The performance of these three models is experimentally evaluated comparing the interpolated association rules with the rules discovered from actual raw data based on two metrics, precision and recall. Our experiments show that the post-order interpolation model provides the highest precision among the three models, and the Kriging method in the pre-order interpolation model presents the highest recall.