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

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.

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).

Value

A db_clust cluster specification.

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.

Examples

# Show all engines
modelenv::get_from_env("db_clust")
#> # A tibble: 2 × 2
#>   engine  mode     
#>   <chr>   <chr>    
#> 1 dbscan  partition
#> 2 hdbscan partition

db_clust()
#> DBSCAN Clustering Specification (partition)
#> 
#> Computational engine: dbscan 
#>