| 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:
group: the clustering partition. If rho>0, some subjects could be in the noise cluster ("K+1")
rho: the threshold for the noise cluster
homogeneity: percentage of homogeneity of the subjects in each cluster and the overall homogeneity
rv_with_compromise: RV coefficient of each subject with its cluster compromise
weights: weight associated with each subject in its cluster
comp_RV: RV coefficient between the compromises associated with the various clusters
compromise: the W compromise of each cluster
coord: the coordinates of objects of each cluster
inertia: percentage of total variance explained by each axis for each cluster
rv_all_cluster: the RV coefficient between each subject and each cluster compromise
criterion: the CLUSTATIS criterion error
param: parameters called
type: parameter passed to other functions
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)