clustatis_FreeSort_kmeans {ClustBlock}R Documentation

Compute the CLUSTATIS partitionning algorithm on free sorting data

Description

Partitionning algorithm for Free Sorting data. Each cluster is associated with a compromise computed by the STATIS method. Moreover, a noise cluster can be set up.

Usage

clustatis_FreeSort_kmeans(Data, NameSub=NULL, clust, nstart=100, rho=0,Itermax=30,
Graph_groups=TRUE, Graph_weights=FALSE,  print_attempt=FALSE)

Arguments

Data

data frame or matrix. Corresponds to all variables that contain subjects results. Each column corresponds to a subject and gives the groups to which the products (rows) are assigned

NameSub

string vector. Name of each subject. Length must be equal to the number of clumn of the Data. If NULL, the names are S1,...Sm. Default: NULL

clust

numerical vector or integer. Initial partition or number of starting partitions if integer. If numerical vector, the numbers must be 1,2,3,...,number of clusters

nstart

integer. Number of starting partitions. Default: 100

rho

numerical between 0 and 1. Threshold for the noise cluster. Default:0

Itermax

numerical. Maximum of iterations by partitionning algorithm. Default: 30

Graph_groups

logical. Should each cluster compromise be plotted? Default: TRUE

Graph_weights

logical. Should the barplot of the weights in each cluster be plotted? Default: FALSE

print_attempt

logical. Print the number of remaining attempts in the multi-start case? Default: FALSE

Value

a list with:

References

Llobell, F., Cariou, V., Vigneau, E., Labenne, A., & Qannari, E. M. (2018). Analysis and clustering of multiblock datasets by means of the STATIS and CLUSTATIS methods. Application to sensometrics. Food Quality and Preference, in Press.
Llobell, F., Vigneau, E., Qannari, E. M. (2019). Clustering datasets by means of CLUSTATIS with identification of atypical datasets. Application to sensometrics. Food Quality and Preference, 75, 97-104.

See Also

clustatis_FreeSort, preprocess_FreeSort, summary.clustatis, , plot.clustatis

Examples

data(choc)
res.clu=clustatis_FreeSort_kmeans(choc, clust=2)
plot(res.clu, Graph_groups=FALSE, Graph_weights=TRUE)
summary(res.clu)


[Package ClustBlock version 2.4.0 Index]