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Setup DBSCANConfig

Source: R/09_S7_ClusteringConfig.R
setup_DBSCAN.Rd

Setup DBSCANConfig

Usage

setup_DBSCAN(
  eps = 0.5,
  min_points = 5L,
  weights = NULL,
  border_points = TRUE,
  search = c("kdtree", "linear", "dist"),
  bucket_size = 100L,
  split_rule = c("SUGGEST", "STD", "MIDPT", "FAIR", "SL_MIDPT", "SL_FAIR"),
  approx = FALSE
)

Arguments

eps

Float: Radius of neighborhood.

min_points

Integer: Minimum number of points in a neighborhood to form a cluster.

weights

Numeric vector: Weights for data points.

border_points

Logical: If TRUE, assign border points to clusters.

Character: Nearest neighbor search strategy: "kdtree", "linear", or "dist".

bucket_size

Integer: Size of buckets for k-dtree search.

split_rule

Character: Rule for splitting clusters: "SUGGEST", "STD", "MIDPT", "FAIR", "SL_MIDPT", "SL_FAIR".

approx

Logical: If TRUE, use approximate nearest neighbor search.

Value

DBSCANConfig object.

Author

EDG

Examples

dbscan_config <- setup_DBSCAN(eps = 0.5, min_points = 5L)
dbscan_config
#> <DBSCAN ClusteringConfig>
#>           eps: <nmr> 0.50
#>    min_points: <int> 5
#>       weights: <NUL> NULL
#> border_points: <lgc> TRUE
#>        search: <chr> kdtree
#>   bucket_size: <int> 100
#>    split_rule: <chr> SUGGEST
#>        approx: <lgc> FALSE