The "partition intersection" fusion method used in SDC may have limitations in scenarios where the clusters are not well-separated or when there is significant overlap between clusters. In such cases, the fusion process may lead to misclassification of objects or inaccurate cluster boundaries. To improve this method, one approach could be to incorporate a weighting mechanism that considers the confidence level of each cluster assignment based on the density or proximity of objects. By assigning weights to different clusters during the fusion process, the algorithm can better capture the uncertainty in cluster assignments and make more informed decisions.