Density-based spatial clustering of applications with noise.
Usage
shoal_dbscan(x, eps = 0.5, min_samples = 5L, metric = c("euclidean", "cosine"))Arguments
- x
A numeric matrix or data frame. Data frames are coerced to a matrix using their numeric columns (non-numeric columns are dropped).
- eps
Neighbourhood radius. Default
0.5.- min_samples
Minimum number of points to form a dense region. Default
5L.- metric
Distance metric, one of
"euclidean"or"cosine".
Value
An object of class c("shoal_dbscan", "shoal_clustering"): a list
with components cluster (integer vector of cluster IDs, NA for noise),
n_clusters, n_noise, data (the input matrix), algorithm, params,
and metric.
See also
shoal_knn(), whose plot() method draws the k-nearest-neighbour
distance curve used to choose eps.
Examples
res <- shoal_dbscan(as.matrix(iris[, 1:4]), eps = 0.5, min_samples = 5L)
res
#>
#> ── DBSCAN Clustering
#> Metric: "euclidean"
#> Parameters: eps = 0.5, min_samples = 5
#> Clusters: 2, Noise points: 17