cluutils: Clustering utilities

In daltoolbox: Leveraging Experiment Lines to Data Analytics

Clustering utilities

Description

Utility object that groups clustering metrics and model-selection helpers.

Usage

cluutils()

Details

The object organizes helpers into two semantic groups:

Metrics

• metric_wcss() computes the total within-cluster sum of squares.

• metric_silhouette() computes the mean silhouette score from pairwise distances.

• metric_entropy() computes external clustering entropy against a reference label.

• metric_purity() computes cluster purity against a reference label.

• metric_davies_bouldin() computes the Davies-Bouldin index.

• metric_calinski_harabasz() computes the Calinski-Harabasz score.

• metric_adjusted_rand_index() computes the adjusted Rand index.

• metric_noise_points() summarizes the number of noise points in density-based clustering.

• metric_loglik() and metric_modularity() expose model-specific quality summaries.

Selectors

• selector_best() selects the best hyperparameter value by direct optimization.

• selector_elbow() selects the elbow of a metric curve via maximum curvature.

Metric helpers return a standardized list with fields metric, value, goal, and type. This keeps the contract uniform even when the metrics themselves differ.

Value

returns a cluutils object exposing metric and selector helpers.

Examples

utils <- cluutils()

data(iris)
x <- iris[, 1:4]
clu <- stats::kmeans(x, centers = 3)$cluster

utils$metric_wcss(x, clu)
utils$metric_silhouette(x, clu)
utils$metric_entropy(clu, iris$Species)
utils$selector_best(c(0.31, 0.42, 0.39), goal = "maximize")

daltoolbox documentation built on May 14, 2026, 9:06 a.m.