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 = FALSE
)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
Logical: If TRUE, use approximate nearest neighbor search.
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