Technology

HDBSCAN

HDBSCAN: A robust, density-based clustering algorithm that finds clusters of varying densities and handles noise effectively.

HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) is a powerful clustering algorithm developed by Dr. Leland McInnes and his team. It extends DBSCAN by converting it into a hierarchical clustering algorithm, then uses a technique to extract a flat partitioning from the hierarchy. This allows HDBSCAN to discover clusters of varying densities in data, unlike algorithms such as k-means. It also inherently handles outliers, marking them as noise rather than forcing them into a cluster. Key advantages include its ability to work with arbitrary distance metrics and its minimal parameterization (primarily requiring only `min_cluster_size`), making it a versatile tool for exploratory data analysis and anomaly detection across diverse datasets.