cata (version 0.1.0.27)

bcluster.h: b-cluster analysis by hierarchical agglomerative strategy

Description

Perform b-clustering using the hierarchical agglomerative clustering strategy.

Usage

bcluster.h(X, measure = "b", runs = 1, seed = 2021)

Value

An object of class hclust from hierarchical b-cluster analysis results (a list of such objects if runs>1), where each hclust

object has the structure described in hclust as well as the item retainedB (a vector indicating the retained sensory differentiation at each iteration (merger)).

Arguments

X

three-way array; the \(I \times J \times M\) array has \(I\) assessors, \(J\) products, \(M\) attributes where CATA data have values 0 (not checked) and 1 (checked)

measure

currently only b (the b-measure) is implemented

runs

number of runs (defaults to 1; use a higher number of runs for a real application)

seed

for reproducibility (default is 2021)

Author

J.C. Castura

References

Castura, J.C., Meyners, M., Varela, P., & Næs, T. (2022). Clustering consumers based on product discrimination in check-all-that-apply (CATA) data. Food Quality and Preference, 104564. tools:::Rd_expr_doi("10.1016/j.foodqual.2022.104564").

Examples

data(bread)

# hierarchical b-cluster analysis on first 8 consumers and first 5 attributes
b <- bcluster.h(bread$cata[1:8,,1:5])

plot(as.dendrogram(b), 
  main = "Hierarchical b-cluster analysis", 
  sub = "8 bread consumers on 5 attributes")