HDBSCAN, short for Hierarchical Density-Based Spatial Clustering of Applications with Noise, is a clustering algorithm that aims to find clusters of varying density in a dataset. It is an extension of the traditional DBSCAN algorithm, which is widely used for density-based clustering.
HDBSCAN uses a hierarchical approach to cluster the data, allowing it to handle clusters of different sizes and shapes. It constructs a hierarchical tree of clusters, where each level of the tree represents a different clustering solution with a varying level of cluster granularity. This flexibility allows HDBSCAN to detect clusters of different densities and capture complex cluster structures within the data.
Additionally, HDBSCAN incorporates a robust method for identifying and handling noisy data points or outliers, which are points that do not belong to any specific cluster. These noisy points are represented as a cluster of their own, allowing for a comprehensive understanding of the dataset.
HDBSCAN is implemented in Python as the hdbscan library, providing a high-performance and easy-to-use tool for clustering analysis. It has gained popularity in various fields, including machine learning, data mining, and spatial data analysis, due to its ability to uncover meaningful patterns and structure within datasets.
Python HDBSCAN - 33 examples found. These are the top rated real world Python examples of hdbscan.HDBSCAN extracted from open source projects. You can rate examples to help us improve the quality of examples.