Comparing DBSCAN, k-means, and Hierarchical Clustering: When and Why To Choose Density-Based Methods

This post details the comparison of DBSCAN, k-means, and hierarchical clustering algorithms, focusing on their methodologies, strengths, and limitations. It provides code examples for k-means and hierarchical clustering using the iris dataset and discusses suitable scenarios for each. The post also introduces DBSCAN's core concepts (core points, border points, noise points) and its parameters (epsilon and MinPts), highlighting its ability to discover clusters of arbitrary shape and handle noise, making it suitable for complex data distributions.