mset_kmeans: Generates Methods Settings for K-Means Clustering

In qcluster: Clustering via Quadratic Scoring

Generates Methods Settings for K-Means Clustering

Description

The function generates a software abstraction of a list of clustering models implemented through a set of tuned methods and algorithms. In particular, it generates a list of kmeans-type functions each combining tuning parameters and other algorithmic settings. The generated functions are ready to be called on the data set.

Usage

mset_kmeans(
  K = c(1:10),
  iter.max = 50,
  nstart = 30,
  algorithm = "Hartigan-Wong",
  trace = FALSE
)

Arguments

K	a vector, specifies the number of clusters.
iter.max	a vector, contains the settings of the iter.max parameter of kmeans.
nstart	a vector, contains the settings of the nstart parameter ofkmeans.
algorithm	a vector, contains the settings of the algorithm parameter of kmeans.
trace	a vector, contains the settings of the trace parameter of kmeans.

Details

The function produces functions implementing competing clustering methods based on the K-Means methodology as implemented in kmeans. This is a specialized version of the more general function mset_user. In particular, it produces a list of kmeans functions each corresponding to a specific setup in terms of hyper-parameters (e.g. the number of clusters) and algorithm's control parameters (e.g. initialization). See kmeans for a detailed description of the role of each argument and their data types.

Each combination of tuning parameters yields one element of the returned qcmethod object.

In the generated fn, the params component is built from the returned partition via clust2params.

Value

An S3 object of class 'qcmethod'. Each element of the list represents a competing method containing the following objects

fullname	a string identifying the setup.
callargs	a list with kmeans function arguments.
fn	the function implementing the specified setting. This fn function can be executed on the data set. It has two arguments: data and only_params. data is a data matrix or data.frame only_params is logical. If only_params==FALSE (default), fn will return the object returned by kmeans, augmented with a params component. If only_params==TRUE (default) fn will return only cluster parameters (proportion, mean, and cov; see clust2params).

References

Coraggio, Luca, and Pietro Coretto (2023). Selecting the Number of Clusters, Clustering Models, and Algorithms. A Unifying Approach Based on the Quadratic Discriminant Score. Journal of Multivariate Analysis, Vol. 196(105181), pp. 1-20, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.jmva.2023.105181")}

See Also

kmeans, mset_user, bqs

Examples

# 'kmeans' settings combining number of clusters K={2,3}
# and numbers of random starts {10,20}
A <- mset_kmeans(K = c(2,3), nstart = c(10,20))

# select setup 1: K=2, nstart = 10
m <- A[[1]]
print(m)

# cluster with the method set in 'm'
data("banknote")
dat  <- banknote[-1]
fit1 <- m$fn(dat)
fit1
class(fit1)

# if only cluster parameters are needed
fit2 <- m$fn(dat, only_params = TRUE)
fit2

qcluster documentation built on June 5, 2026, 5:07 p.m.