Large earthquakes are often followed by aftershocks, which can cause further damage and cost lives. A pattern recognition approach called Next Strong Related Earthquake (NESTORE) has been developed to forecast whether one of these subsequent large events is to be expected in an occurring seismicity cluster. This method, already successfully applied in Italy, Slovenia, California, Greece, and Japan, has been optimised in the NESTOREv1.0 software written in MATLAB. Using machine learning, NESTOREv1.0 provides a probabilistic forecast of earthquake clusters where a mainshock is followed by a significant aftershock. It classifies clusters as type A (mainshock and strongest aftershock differ by ≤ 1 magnitude unit) or type B (larger difference). NESTOREv1.0 adapts to specific regions through supervised training. It trains one-node decision trees on individual features at increasing time intervals, selects the best classifiers, and combines them using a Bayesian method to forecast type A clusters. Recent improvements to the algorithm added a new approach for identifying clusters based on Epidemic-Type Aftershock Sequence (ETAS) and an innovative method for detecting outliers before training. This study compares results from Greece, Italy, western Slovenia, California, and Japan, highlighting the performance on independent test sets and seismicity features in different regions and interpreting the differences between the regions.