The single-dimensional strategy in SDC can be extended to handle other types of specialized datasets by adapting the concept of dimension splitting and partition intersection to suit the specific characteristics of those datasets. For example, in datasets with high-dimensional features, the single-dimensional strategy can be modified to consider subsets of dimensions rather than individual dimensions. This way, the clustering process can be performed on these subsets to capture the underlying patterns in the data. Additionally, for datasets with categorical features, the strategy can be adjusted to handle the unique nature of categorical data by defining appropriate distance metrics or similarity measures for clustering.