Density-Based Spatial Clustering of Applications with Noise

Find clusters and outliers by using the DBSCAN algorithm

DBSCAN uses a density-based approach to find arbitrarily shaped clusters and outliers (noise) in data. This technique is useful when you do not know the number of clusters in advance. Use the dbscan function to perform clustering on an input data matrix or on pairwise distances between observations.

Functions

dbscanDensity-based spatial clustering of applications with noise (DBSCAN)

Topics

Introduction to Cluster Analysis

Understand the basic types of cluster analysis.

DBSCAN

Group data into clusters and identify outliers by using a density-based approach.