Example Concept: K-Means is efficient for large datasets due to its linear time complexity, making it suitable for clustering when the number of clusters is known. DBSCAN is effective for identifying clusters of varying shapes and sizes, particularly in datasets with noise, but can be computationally expensive for very large datasets. Hierarchical clustering provides a dendrogram for better interpretability but is typically not scalable for large datasets due to its quadratic time complexity.