The Machine Learning Advent Calendar Day 10: DBSCAN in Excel

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) demonstrates the power of a straightforward approachcounting neighboring points within a fixed radiusto identify clusters and anomalies without relying on probabilistic models, even functioning effectively within Excel. However, its dependence on a single, fixed radius limits its robustness in real-world datasets, prompting the development of HDBSCAN, an advanced variant that adapts to varying data densities for more reliable clustering. This progression highlights how simple density-based methods can be enhanced to handle complex, noisy data environments, broadening their applicability in practical machine learning tasks.