Concept

5 articles

Density Reachability · Core Points, Border Points and the ε-Neighbourhood Definition

The eps-neighbourhood, core points, border points and noise written out precisely, with a replaceable distance function and a counted example.

HDBSCAN · Hierarchical Density-Based Clustering Explained

A complete guide to HDBSCAN — how it builds a hierarchy of DBSCAN clusterings, the mutual reachability graph, cluster stability, and the min_cluster_size parameter.

Noise Points in Clustering · Outlier Detection vs Noise Classification

How density-based clustering identifies noise — the difference between outliers and noise, the role of noise points in anomaly detection, and interpreting noise classifications.

OPTICS · Ordering Points To Identify the Clustering Structure

How OPTICS extends DBSCAN to handle varying-density clusters — the reachability plot, core distance, and how to extract clusters from OPTICS output.

DBSCAN · Density-Based Spatial Clustering Explained

A complete guide to the DBSCAN clustering algorithm — how density-based clustering works, the eps and minPts parameters, advantages over k-means, and handling of noise points.