qcluster (version 1.2.1)

mset_pam: 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)

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 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 pam. If only_params==TRUE (default) fn will return only cluster parameters (proportions, mean, and cov, see clust2params.

Arguments

K

a vector/list, specifies the number of clusters.

metric

a vector, contains the settings of the metric parameter of pam.

medoids

list, contains the settings of the medoids parameter of pam.

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 more detail for a detailed description of the role of each argument and their data types.

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, 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