DBSCAN Clustering: A Detailed Guide and Application

Clustering is a fundamental aspect of unsupervised learning, used to identify groups of similar data points within a dataset. While algorithms like K-Means are widely known, they may struggle with identifying clusters of arbitrary shape and handling noise in the data. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a robust density-based clustering algorithm known for its ability to find non-linear clusters and effectively handle outliers.

In this blog, we’ll explore how DBSCAN works, its advantages, limitations, and demonstrate its practical application using Python.

What is DBSCAN?

DBSCAN is a density-based clustering algorithm that groups together data points that are closely packed, marking as outliers those points that lie alone in low-density regions. Unlike K-Means, which requires you to specify the number of clusters, DBSCAN uses two main parameters:

• eps (epsilon): The maximum distance between two samples for one to be considered as part of the neighborhood of the other.
• min_samples: The minimum number of points required to form a dense region (core point).

Core Concepts of DBSCAN:

• Core Points: A point is a core point if it has at least min_samples…