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Tidy DBSCAN Clustering

Source: R/unsupervised-dbscan.R
tidy_dbscan.Rd

Performs density-based clustering with tidy output

Usage

tidy_dbscan(data, eps, minPts = 5, cols = NULL, distance = "euclidean")

Arguments

data

A data frame, tibble, numeric matrix, or dist object

eps

Neighborhood radius (epsilon)

minPts

Minimum number of points to form a dense region (default: 5)

cols

Columns to include (tidy select). If NULL, uses all numeric columns, or every column for distance = "gower".

distance

Distance metric if data is not a dist object (default: "euclidean"): any method dist accepts, or "gower" for mixed data types

Value

A list of class "tidy_dbscan" containing:

  • clusters: tibble with observation IDs, cluster assignments (0 = noise), and the logical flags is_noise and is_core

  • summary: tibble with each cluster's size and number of core points

  • n_clusters: number of clusters (excluding noise)

  • n_noise: number of noise points

  • eps, minPts: the parameters used

  • model: original dbscan object

Examples

# Basic DBSCAN
db_result <- tidy_dbscan(iris, eps = 0.5, minPts = 5)

# With suggested eps from k-NN distance plot
eps_suggestion <- suggest_eps(iris, minPts = 5)
db_result <- tidy_dbscan(iris, eps = eps_suggestion$eps, minPts = 5)