What are the differences between DBSCAN and K-Means clustering?

What are the differences between DBSCAN and K-Means clustering?

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and K-Means are both clustering algorithms, but they differ significantly. K-Means partitions data into K clusters based on distance to the centroid, while DBSCAN groups points that are closely packed together, marking points in low-density regions as outliers.

When should I use DBSCAN instead of K-Means?

You should use DBSCAN when you have clusters of varying shapes and sizes, or when you expect noise in your data. K-Means works best with spherical clusters and when you know the number of clusters in advance.

Can you explain how DBSCAN handles noise?

DBSCAN identifies noise by looking for points that are not part of any cluster. If a point is not within the specified distance of any core point (a point with a minimum number of neighbors), it is classified as noise.

What are the limitations of K-Means?

K-Means has several limitations, including sensitivity to outliers, the need to specify the number of clusters beforehand, and difficulty in identifying non-spherical clusters.