DBSCAN: Intution, Advantages, and Points to Remember

1. INTUITION

Imagine you have a field of stars in the night sky, and you want to group them based on how densely they are packed together rather than a predetermined number of clusters. This is where DBSCAN, which stands for Density-Based Spatial Clustering of Applications with Noise, shines like a cosmic beacon.

DBSCAN is a remarkable clustering algorithm that doesn’t rely on predefining the number of clusters, making it particularly well-suited for finding clusters of varying shapes and sizes in your data.

2. WORKING

Here’s how DBSCAN works:

1. Density-Centered Clustering: DBSCAN identifies clusters by looking at the density of data points. It defines a cluster as a dense region of data points that is separated by areas of lower point density.
2. Core Points: The algorithm starts by selecting a random data point and examines its neighborhood within a specified radius (epsilon, ε). If there are at least a minimum number of data points (minPts) within this neighborhood, it marks the central point as a “core point.”
3. Growing Clusters: DBSCAN then expands the cluster around this core point by recursively adding nearby points that are also core points. This process continues until no more core…