ICSClust {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 |
nb_clusters |
the number of clusters searched for. |
ICS_args |
list of |
criterion |
criterion to automatically decide which invariant components
to keep. Possible values are |
ICS_crit_args |
list of arguments passed to |
method |
clustering method to perform. Currently implemented wrapper
functions are |
clustering_args |
list of |
clusters |
a vector indicating the true clusters of the data. By default,
it is |
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"
. SeeICS
-
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 (from1: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..
See Also
med_crit()
, normal_crit()
,
var_crit()
, ICS,
discriminatory_crit()
, kmeans_clust()
,
tkmeans_clust()
, pam_clust()
,
rimle_clust()
, mclust_clust()
summary()
and plot()
methods
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)