Intermediate · Data
Density-based spatial clustering of applications with noise (DBSCAN)
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A clustering algorithm that groups together points that are closely packed, marking points in low-density regions as outliers.
Technical Definition
A clustering algorithm that groups together points that are closely packed, marking points in low-density regions as outliers.
How it works
DBSCAN is a popular unsupervised learning algorithm used for data clustering. It identifies clusters as areas of high density separated by areas of low density. A key advantage of DBSCAN is its ability to find arbitrarily shaped clusters and identify noise points that do not belong to any cluster.
Related Concepts
- Clustering — An unsupervised technique that groups similar data points without using labels.
- Unsupervised Learning — Learning patterns from data that has no labels — only the inputs.
- Data mining — Discovering patterns and insights in large datasets using methods from machine learning, statistics, and databases.