An overview of clustering algorithms, including centroid-based (K-Means, K-Means++), density-based (DBSCAN), hierarchical, and distribution-based clustering. The article explains how each type works, its pros and cons, provides code examples, and discusses use cases.
A simple and intuitive explanation of DBSCAN (Density-Based Spatial Clustering of Applications with Noise), a clustering algorithm that can identify outliers, extract new features, compress data, and perform novelty detection. The article provides a fast implementation of DBSCAN in Python.
