db_clust: Density-Based Spatial Clustering of Applications with Noise...

In tidyclust: A Common API to Clustering

Density-Based Spatial Clustering of Applications with Noise (DBSCAN)

Description

db_clust defines a model that fits clusters based on areas with observations that are densely packed together using the DBSCAN algorithm

There are multiple implementations for this model, and the implementation is chosen by setting the model engine. The engine-specific pages for this model are listed below.

• dbscan

• hdbscan

Usage

db_clust(
  mode = "partition",
  engine = "dbscan",
  radius = NULL,
  min_points = NULL
)

Arguments

mode	A single character string for the type of model. The only possible value for this model is "partition".
engine	A single character string specifying what computational engine to use for fitting. The engine for this model is "dbscan".
radius	Positive double, Radius drawn around points to determine core-points and cluster assignments (required).
min_points	Positive integer, Minimum number of connected points required to form a core-point, including the point itself (required).

Details

What does it mean to predict?

To predict the cluster assignment for a new observation, we determine if a point is within the radius of a core point. If so, we predict the same cluster as the core point. If not, we predict the observation to be an outlier.

Value

A db_clust cluster specification.

Examples

# Show all engines
modelenv::get_from_env("db_clust")

db_clust()

tidyclust documentation built on June 20, 2026, 9:08 a.m.