ICSClust: Tandem clustering with ICS
In ICSClust: Tandem Clustering with Invariant Coordinate Selection

ICSClust

R Documentation

Tandem clustering with ICS

Description

Sequential clustering approach: (i) dimension reduction through the Invariant Coordinate Selection method using the ICS function and (ii) clustering of the transformed data.

Usage

ICSClust(
  X,
  nb_select = NULL,
  nb_clusters = NULL,
  ICS_args = list(),
  criterion = c("med_crit", "normal_crit", "var_crit", "discriminatory_crit"),
  ICS_crit_args = list(),
  method = c("kmeans_clust", "tkmeans_clust", "pam_clust", "mclust_clust",
    "rmclust_clust", "rimle_clust"),
  clustering_args = list(),
  clusters = NULL
)

Arguments

`X`	a numeric matrix or data frame containing the data.
`nb_select`	the number of components to select. It is used only in case `criterion` is either `"med_crit"`, `"var_crit"` or `"discriminatory_crit"`. By default it is set to `NULL`, i.e the number of components to select is the number of clusters minus one.
`nb_clusters`	the number of clusters searched for.
`ICS_args`	list of `ICS-S3` arguments. Otherwise, default values of `ICS-S3` are used.
`criterion`	criterion to automatically decide which invariant components to keep. Possible values are `"med_crit"`, `"normal_crit"`, `"var_crit"` and `"discriminatory_crit"`. The default value is `"med_crit"`. See `med_crit()`, `normal_crit()`, `var_crit()` or `discriminatory_crit()` for more details.
`ICS_crit_args`	list of arguments passed to `med_crit()`, `normal_crit()`, `var_crit()` or `discriminatory_crit()` for choosing the components to keep.
`method`	clustering method to perform. Currently implemented wrapper functions are `"kmeans_clust"`, `"tkmeans_clust"`, `"pam_clust"`, `"mclust_clust"`, `"rmclust_clust"` or `"rimle_clust"`. The default value is `"kmeans_clust"`.
`clustering_args`	list of `kmeans_clust()`, `tkmeans_clust()`, `pam_clust()`, `rimle_clust()`, `mclust_clust()` or `rmclust_clust()` arguments for performing cluster analysis.
`clusters`	a vector indicating the true clusters of the data. By default, it is `NULL` but it is required to choose the components based on the discriminatory criterion `discriminatory_crit`.

Details

Tandem clustering with ICS is a sequential method:

ICS is performed.
only a subset of the first and/or the last few components are selected based on a criterion.
the clustering method is performed only on the subspace of the selected components.
wrapper for several different clustering methods are provided. Users can however also write wrappers for other clustering methods.

Value

An object of class "ICSClust" with the following components:

ICS_out: An object of class "ICS". See ICS
select: a vector of the names of the selected invariant coordinates.
clusters: a vector of the new partition of the data, i.e a vector of integers (from 1:k) indicating the cluster to which each observation is allocated. 0 indicates outlying observations.

summary() and plot() methods are available.

Author(s)

Aurore Archimbaud

References

Alfons, A., Archimbaud, A., Nordhausen, K., & Ruiz-Gazen, A. (2022). Tandem clustering with invariant coordinate selection. arXiv preprint arXiv:2212.06108..

Examples

X <- iris[,1:4]

# indicating the number of components to retain for the dimension reduction
# step as well as the number of clusters searched for.
out <- ICSClust(X, nb_select = 2, nb_clusters = 3)
summary(out)
plot(out)

# changing the scatter pair to consider in ICS
out <- ICSClust(X, nb_select = 1, nb_clusters = 3,
ICS_args = list(S1 = ICS_mcd_raw, S2 = ICS_cov,S1_args = list(alpha = 0.5)))
summary(out)
plot(out)
 
# changing the criterion for choosing the invariant coordinates
out <- ICSClust(X, nb_clusters = 3, criterion = "normal_crit",
ICS_crit_args = list(level = 0.1, test = "anscombe.test", max_select = NULL))
summary(out)
plot(out)

# changing the clustering method
out <- ICSClust(X, nb_clusters = 3, method  = "tkmeans_clust", 
clustering_args = list(alpha = 0.1))
summary(out)
plot(out)

ICSClust documentation built on Sept. 21, 2023, 5:07 p.m.