Divisive clustering, also known as top-down clustering, is a hierarchical clustering method that begins with all data points in a single cluster and recursively splits them into smaller clusters. This approach is computationally intensive but can be more effective than agglomerative methods in identifying meaningful patterns when the data naturally divides into distinct groups.