What is HDBSCAN?
HDBSCAN is a clustering algorithm that is designed to uncover clusters in datasets based on the density distribution of data points. Unlike some other clustering methods, it doesn’t requires specifying the number of clusters in advance, making it more adaptable to different datasets. It uses high-density regions to identify clusters and views isolated or low-density points as noise. HDBSCAN is especially helpful for datasets with complex structures or varying densities because it creates a hierarchical tree of clusters that enable users to examine the data at different levels of granularity.
Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN)
Clustering is a machine-learning technique that divides data into groups, or clusters, based on similarity. By putting similar data points together and separating dissimilar points into separate clusters, it seeks to uncover underlying structures in datasets.
In this article, we will focus on the HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) technique. Like other clustering methods, HDBSCAN begins by determining the proximity of the data points, distinguishing the regions with high density from sparse regions. But what distinguishes HDBSCAN from other methods is its capacity to dynamically adjust to the different densities and forms of clusters in the data, producing more reliable and adaptable clustering results.