setup_DBSCAN: Setup DBSCANConfig

In rtemis: Schema-Driven Machine Learning and Visualization

Setup DBSCANConfig

Description

Setup DBSCANConfig

Usage

setup_DBSCAN(
  eps = 0.5,
  min_points = 5L,
  weights = NULL,
  border_points = TRUE,
  search = "kdtree",
  bucket_size = 100L,
  split_rule = "SUGGEST",
  approx = 0,
  features = NULL
)

Arguments

eps	Numeric (0, Inf): Radius of neighborhood.
min_points	Integer [1, Inf): Minimum number of points in a neighborhood to form a cluster.
weights	Optional Numeric vector: Weights for data points.
border_points	Logical: If TRUE, assign border points to clusters.
search	Character {"kdtree", "linear", "dist"}: Nearest neighbor search strategy.
bucket_size	Integer [1, Inf): Size of buckets for k-d tree search.
split_rule	Character {"SUGGEST", "STD", "MIDPT", "FAIR", "SL_MIDPT", "SL_FAIR"}: Rule for splitting the k-d tree.
approx	Numeric [0, Inf): Approximation factor for the nearest neighbor search (dbscan::frNN(approx =)); 0 searches exactly. A positive value guarantees only the neighbors within eps / (1 + approx) and may omit others within eps, which can produce spurious clusters and noise points in exchange for speed.
features	Optional Character vector: Names of at least 2 distinct feature columns to cluster on. NULL clusters on all numeric features.

Value

DBSCANConfig object.

Author(s)

EDG

Examples

dbscan_config <- setup_DBSCAN(eps = 0.5, min_points = 5L)
dbscan_config

rtemis documentation built on Oct. 20, 2026, 1:06 a.m.