cluster_kmeans: k-means
In daltoolbox: Leveraging Experiment Lines to Data Analytics

cluster_kmeans

R Documentation

k-means

Description

k-means clustering using stats::kmeans.

Usage

cluster_kmeans(k = 1)

Arguments

`k`	the number of clusters to form.

Details

Partitions data into k clusters minimizing within‑cluster sum of squares. The intrinsic quality metric returned is the total within‑cluster SSE (lower is better).

Value

returns a k-means object.

References

MacQueen, J. (1967). Some Methods for classification and Analysis of Multivariate Observations. Lloyd, S. (1982). Least squares quantization in PCM.

Examples

# setup clustering
model <- cluster_kmeans(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.