ClustBlock (version 4.0.0)

ClusMB: Perform a cluster analysis of rows in a Multi-block context with the ClusMB method

Description

Clustering of rows (products in sensory analysis) in a Multi-block context. The hierarchical clustering is followed by a partitioning algorithm (consolidation).

Usage

ClusMB(Data, Blocks, NameBlocks=NULL, scale=FALSE, center=TRUE,
nclust=NULL, gpmax=6)

Value

• group: the clustering partition after consolidation.

• nbgH: Advised number of clusters per Hartigan index

• nbgCH: Advised number of clusters per Calinski-Harabasz index

• cutree_k: the partition obtained by cutting the dendrogram in K clusters (before consolidation).

• dend: The ClusMB dendrogram

• param: parameters called

• type: parameter passed to other functions

Arguments

Data

data frame or matrix. Correspond to all the blocks of variables merged horizontally

Blocks

numerical vector. The number of variables of each block. The sum must be equal to the number of columns of Data.

NameBlocks

string vector. Name of each block. Length must be equal to the length of Blocks vector. If NULL, the names are B1,...Bm. Default: NULL

scale

logical. Should the data variables be scaled? Default: FALSE

center

logical. Should the data variables be centered? Default: TRUE. Please set to FALSE for a CATA experiment

nclust

numerical. Number of clusters to consider. If NULL, the Hartigan index advice is taken.

gpmax

logical. What is maximum number of clusters to consider? Default: min(6, number of blocks -2)

References

Llobell, F., Qannari, E.M. (June 10, 2022). Cluster analysis in a multi-bloc setting. SMTDA, Athens, Greece.
Llobell, F., Giacalone, D., Qannari, E. M. (Pangborn 2021). Cluster Analysis of products in CATA experiments.
Paper submitted

See Also

indicesClusters, summary.clusRows , clustRowsOnStatisAxes

Examples

#####projective mapping####
library(ClustBlock)
data(smoo)
res1=ClusMB(smoo, rep(2,24))
summary(res1)
indicesClusters(smoo, rep(2,24), res1$group)

####CATA####
data(fish)
Data=fish[1:66,2:30]
chang2=change_cata_format2(Data, nprod= 6, nattr= 27, nsub = 11, nsess= 1)
res2=ClusMB(Data= chang2$Datafinal, Blocks= rep(27, 11), center=FALSE)
indicesClusters(Data= chang2$Datafinal, Blocks= rep(27, 11),cut = res2$group, center=FALSE)