Fundamentally distinct clustering techniques, K-means and DBSCAN, each have their own advantages and disadvantages. Because K-means depends on centroid-based partitioning and needs the user to predefine the number of clusters, it works best in scenarios where the number of clusters is known and the shapes are generally spherical. It is less efficient, nevertheless, and sensitive to initializations when handling clusters with unusual shapes. However, DBSCAN, a density-based technique, works well at finding clusters of any shape and doesn’t require a predetermined number of clusters. It’s a good option for more intricate and varied data clustering tasks because of its great ability to handle noise and outliers and classify them as unassigned data points. The particulars of the data and the intended clustering objectives should be taken into consideration while selecting between K-means and DBSCAN.