cluscata_kmeans_jar: Perform a cluster analysis of subjects in a JAR experiment.

In ClustBlock: Clustering of Datasets

Perform a cluster analysis of subjects in a JAR experiment.

Description

Partitionning of subject from a JAR experiment. Each cluster is associated with a compromise computed by the CATATIS method. Moreover, a noise cluster can be set up.

Usage

cluscata_kmeans_jar(Data, nprod, nsub, levelsJAR=3, beta=0.1, clust, nstart=100, rho=0,
Itermax=30, Graph_groups=TRUE, print_attempt=FALSE, Warnings=FALSE)

Arguments

Data	data frame where the first column is the Assessors, the second is the products and all other columns the JAR attributes with numbers (1 to 3 or 1 to 5, see levelsJAR)
nprod	integer. Number of products.
nsub	integer. Number of subjects.
levelsJAR	integer. 3 or 5 levels. If 5, the data will be transformed in 3 levels.
beta	numerical. Parameter for agreement between JAR and other answers. Between 0 and 0.5.
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	numerical. Number of starting partitions. Default: 100
rho	numerical between 0 and 1. Threshold for the noise cluster. If 0, there is no noise cluster. Default: 0
Itermax	numerical. Maximum of iterations by partitionning algorithm. Default: 30
Graph_groups	logical. Should each cluster compromise graphical representation be plotted? Default: TRUE
print_attempt	logical. Print the number of remaining attempts in multi-start case? Default: FALSE
Warnings	logical. Display warnings about the fact that none of the subjects in some clusters checked an attribute or product? 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

• s_with_compromise: Similarity coefficient of each subject with its cluster compromise

• weights: weight associated with each subject in its cluster

• compromise: The compromise of each cluster

• CA: The correspondance analysis results on each cluster compromise (coordinates, contributions...)

• inertia: percentage of total variance explained by each axis of the CA for each cluster

• s_all_cluster: the similarity coefficient between each subject and each cluster compromise

• param: parameters called

• criterion: the CLUSCATA criterion error

• type: parameter passed to other functions

References

Llobell, F., Vigneau, E. & Qannari, E. M. ((September 14, 2022). Multivariate data analysis and clustering of subjects in a Just about right task. Eurosense, Turku, Finland.

See Also

plot.cluscata, summary.cluscata , catatis_jar, preprocess_JAR, cluscata_jar

Examples

data(cheese)
res=cluscata_kmeans_jar(Data=cheese, nprod=8, nsub=72, levelsJAR=5, clust=4)
#plot(res)
summary(res)

ClustBlock documentation built on Aug. 30, 2023, 5:08 p.m.