CLV3W_kmeans function

Partitioning algorithm of a set of variables (associated with mode 2) oh a three-way array

Each group of variables is associated with a one-rank PARAFAC model (comp x loading x weight). Moreover, a Non Negativity (NN) constraint may be added to the model, so that the loading coefficients have positive values. Return an object of class clv3w.

CLV3W_kmeans(X,K,mode.scale=0,NN=FALSE,init=10,cp.rand=5)

Arguments

• X: : a three way array - variables of mode 2 will be clustered

• K: : number of clusters

• mode.scale: : scaling parameter applied to X, by default centering of X (for mode 2 x mode 3) is done. By default no scaling (mode.scale=0)

  0 : no scaling only centering - the default

  1 : scaling with standard deviation of (mode 2 x mode 3) elements

  2 : global scaling (each block i.e. each mode 2 slice will have the same inertia )

  3 : global scaling (each block i.e. each mode 3 slice will have the same inertia )

• NN: : non Negativity constraint to be added on the loading coefficients. By default no constraint (NN=FALSE)

  TRUE : a non negativity constrained is applied on the loading coefficients to set them as positive values

  FALSE : loading coefficients may be either positive or negative

• init: : either the number of random starts i.e. partitions generated for the initialisation (By default init=10)

• cp.rand: : number of random starts associated with the one rank Candecomp/Parafac model (By default cp.rand=10)

Returns

• results: * clusters: in line 1, the groups membership in the initial partition; in line 2 the final groups membership

  • comp: the latent components of the clusters associated with the first mode
  • loading: the vector of loadings associated with the second mode by cluster
  • weigth: the vector of weights associated with the third mode by cluster
  • criterion: vector of loss giving for each cluster the residual amount between the sub-array and its reconstitution associated with the cluster one rank PARAFAC model
  • niter: number of iterations of the partitioning alorithm

• param: contains the clustering parameters

  • X: the scaled three-way array

call : call of the method

Examples

data(coffee)
## Cluster Analysis of coffee sensory descriptors with block scaling
## to set the assessors to the same footing
res.coffee <- CLV3W_kmeans(coffee,K=2,NN=TRUE,mode.scale=3,init=1,cp.rand=1)
summary(res.coffee)
get_partition(res.coffee)

References

Wilderjans, T. F., & Cariou, V. (2016). CLV3W: A clustering around latent variables approach to detect panel disagreement in three-way conventional sensory profiling data. Food quality and preference, 47, 45-53.

Cariou, V., & Wilderjans, T. F. (2018). Consumer segmentation in multi-attribute product evaluation by means of non-negatively constrained CLV3W. Food Quality and Preference, 67, 18-26.

See Also

summary.clv3W, print.clv3W

Author(s)

Veronique Cariou, veronique.cariou@oniris-nantes.fr