ClustBlock (version 2.3.1)

clustatis_FreeSort_kmeans: 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

Run this code
# NOT RUN {
data(choc)
res.clu=clustatis_FreeSort_kmeans(choc, clust=2)
plot(res.clu, Graph_groups=FALSE, Graph_weights=TRUE)
summary(res.clu)

# }

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