Python for Machine Learning - The Complete Beginners Course - Density-Based Clustering

The video tutorial explains the Dbscan algorithm, a density-based clustering method that identifies clusters of arbitrary shapes and is robust to noise. It operates using two parameters: radius (epsilon) and minimum points (M). The algorithm picks a point and checks if there are at least M points within the radius of epsilon to form a cluster. Dbscan can discover clusters surrounded by different clusters and is effective in noise detection.

What are some advantages of using the Dbscan algorithm for clustering?

How does the Dbscan algorithm handle noise in the data?