Clustered seizures are prevalent among people with epilepsy and can increase mortality risk. While past research has mainly focused on seizure cluster detection, a few recent studies predict seizure clustering by determining whether there will be more seizures in the next 24 hours after the termination of a seizure. Moreover, personalized prediction of clustered seizures in the presence of limited and imbalanced data remains an outstanding problem. We address this problem using a novel transfer learning model to predict seizure clustering within a 24-hour window. To compensate for the limited and imbalanced available data, for each target patient, the model combines trained individual-level predictive models of the target patient and two other patients whose seizure patterns are similar to those of the target patient. Approximate Kullback-Leibler divergence is used to measure the similarity between patients in high-dimensional data. The proposed model is evaluated on a long-term ambulatory intracranial EEG dataset. Compared with individualized predictive models, the proposed model improves F1 scores for patients with limited or highly imbalanced data by up to 51.0%. In addition, the proposed model achieves an average F1 score of 0.702 and an area under the precision-recall curve of 0.809. Our model can be clinically helpful in guiding the treatment of clustered seizures.