mixclustatis: Perform a cluster analysis of variables

In ClustBlock: Clustering of Datasets

Perform a cluster analysis of variables

Description

Perform cluster analysis of variables in context of quantitative, qualitative or mixed datasets with MixCluStatis.

Usage

mixclustatis(Data, quanti=NULL,quali=NULL,Noise_cluster=FALSE,
Itermax=20, printlevel=FALSE, Graph_dend=TRUE, Graph_bar=TRUE,
gpmax=min(6, length(quali)+length(quanti)-1), rhoparam = NULL)

Arguments

Data	data frame or matrix. Correspond to all the data (variables are columns)
quanti	numerical vector. The number of the columns containing quantitative variables.
quali	numerical vector. The number of the columns containing qualitative variables.
Noise_cluster	logical. Should a noise cluster be computed? Default: FALSE
Itermax	numerical. Maximum of iteration for the partitioning algorithm. Default: 30
printlevel	logical. Print the number of remaining levels during the hierarchical clustering algorithm? Default: FALSE
Graph_dend	logical. Should the dendrogram be plotted? Default: TRUE
Graph_bar	logical. Should the barplot of the difference of the criterion and the barplot of the overall homogeneity at each merging step of the hierarchical algorithm be plotted? Default: TRUE
gpmax	logical. What is maximum number of clusters to consider? Default: min(6, number of variables -2)
rhoparam	numerical or vector. What is the threshold for the noise cluster? Between 0 and 1, high value can imply lot of vatriables set aside. If NULL, automatic threshold is computed. Can be different for each group (in this case, provide a vector)

Value

Each partitionK contains a list for each number of clusters of the partition, K=1 to gpmax with:

• group: the clustering partition of variables after consolidation. If Noise_cluster=TRUE, some variables could be in the noise cluster ("K+1")

• rho: the threshold(s) for the noise cluster (computed or input parameter)

• homogeneity: homogeneity index (

• rv_with_compromise: RV coefficient of each variable with its cluster compromise

• weights: weight associated with each variable 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 variable and each cluster compromise

• criterion: the CLUSTATIS criterion error

• param: parameters called in the consolidation

• type: parameter passed to other functions

There is also at the end of the list:

• dend: The CLUSTATIS dendrogram

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

• overall_homogeneity_ng: percentage of overall homogeneity by number of clusters before consolidation (and after if there is no noise cluster)

• diff_crit_ng: variation of criterion when a merging is done before consolidation (and after if there is no noise cluster)

• param: parameters called

• type: parameter passed to other functions

References

Paper submitted: Llobell, F., Abdi, H., Eslami, A. (2026). Clustering of categorical and mixed data variables around latent variables. 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. Llobell, F., & Qannari, E. M. (2020). CLUSTATIS: Cluster analysis of blocks of variables. Electronic Journal of Applied Statistical Analysis, 13(2).

See Also

clustatis, plot.clustatis, summary.clustatis

Examples

data("wine", package = "FactoMineR")
res=mixclustatis(wine, quanti = 3:29, quali = 1:2)
summary(res)
plot(res, Graph_groups = FALSE, Graph_weights = TRUE)

ClustBlock documentation built on June 4, 2026, 5:08 p.m.