cluster_pam: PAM (Partitioning Around Medoids)

In daltoolbox: Leveraging Experiment Lines to Data Analytics

PAM (Partitioning Around Medoids)

Description

Clustering around representative data points (medoids) using cluster::pam.

Usage

cluster_pam(k = 1)

Arguments

k	the number of clusters to generate.

Details

More robust to outliers than k‑means. The intrinsic metric reported is the within‑cluster SSE to medoids.

Value

returns PAM object.

References

Kaufman, L. and Rousseeuw, P. J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis.

Examples

# setup clustering
model <- cluster_pam(k = 3)

#load dataset
data(iris)

# build model
model <- fit(model, iris[,1:4])
clu <- cluster(model, iris[,1:4])
table(clu)

# evaluate model using external metric
eval <- evaluate(model, clu, iris$Species)
eval

daltoolbox documentation built on Nov. 5, 2025, 7:09 p.m.