mset_pam: Generates Methods Settings for Partitioning Around Medoids...

In qcluster: Clustering via Quadratic Scoring

Generates Methods Settings for Partitioning Around Medoids (Pam) Clustering

Description

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

Usage

mset_pam(
  K = seq(10),
  metric = "euclidean",
  medoids = if (is.numeric(nstart)) "random",
  nstart = if (variant == "faster") 1 else NA,
  stand = FALSE,
  do.swap = TRUE,
  variant = "original",
  pamonce = FALSE
)

Arguments

K	a vector/list, specifies the number of clusters.
metric	a vector, contains the settings of the metric parameter of pam.
medoids	settings of the medoids parameter of pam. This can be a character vector, a numeric vector of user-supplied medoid labels, or a list combining these.
nstart	a vector, contains the settings of the nstart parameter of pam.
stand	a vector, contains the settings of the stand parameter of pam.
do.swap	a vector, contains the settings of the do.swap parameter of pam.
variant	a list, contains the settings of the variant parameter of pam.
pamonce	a vector, contains the settings of the pamonce parameter of pam.

Details

The function produces functions implementing competing clustering methods based on the PAM clustering methodology as implemented in pam. This is a specialized version of the more general function mset_user. In particular, it produces a list of pam 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 pam 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.

When medoids is numeric or a list containing numeric entries, the corresponding number of clusters is derived from the supplied labels.

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 pam function arguments.
fn	the function implementing the specified setting. This fn function can be executed on the data set. It has arguments data, diss, cluster.only, keep.data, keep.diss, and only_params. data is a data matrix or data.frame and only_params is logical. If only_params==FALSE (default), fn will return the object returned by pam, 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

pam,mset_user, bqs

Examples

# 'pam' settings combining number of clusters K={2,3}, and dissimilarities {euclidean, manhattan}
A <- mset_pam(K = c(2,3), metric = c("euclidean", "manhattan"))

# select setup 1: K=2, metric = "euclidean"
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
fit1b <- m$fn(dat, only_params = TRUE)
fit1b

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