MultiCons {doMIsaul}  R Documentation 
Performs MultiCons clustering, from AlNajdi et Al.
For some reason, if you want to use mclust()
clustering, the package
needs to be loaded manually
MultiCons(
DB,
Clust_entry = FALSE,
Clustering_selection = c("kmeans", "pam", "OPTICS", "agghc", "AGNES", "DIANA",
"MCLUST", "CMeans", "FANNY", "BaggedClust"),
num_algo = 10,
maxClust = 10,
sim.indice = "Jaccard",
returnAll = FALSE,
Plot = TRUE,
verbose = FALSE
)
DB 
Either data or dataframe of partitions. 
Clust_entry 
Is DB partitions ( 
Clustering_selection 
If DB is data, clustering algorithm to select among. Must be included in default value. 
num_algo 
Number of clustering algorithms to perform. 
maxClust 
Maximum number of clusters. 
sim.indice 
Index for defining best partition. Passed to

returnAll 
Should all partitions ( 
Plot 
Should tree be plotted. 
verbose 
Passed on to 
A list of 2: performances and partitions. If returnAll
is
TRUE
, both elements of the list contain results for all levels of
the tree, else they only contain the results for the best level of
the tree.
library(mclust)
### With clustering algorithm choices
MultiCons(iris[, 1:4],
Clustering_selection = c("kmeans", "pam", "DIANA", "MCLUST"),
Plot = TRUE)
### With a manual clustering entry
parts < data.frame(factor(rep(c(1,2,3), each = 50)),
factor(rep(c(1,2,3), times = c(100, 25, 25))),
factor(rep(c(1,2), times = c(50, 100))),
factor(rep(c(3, 2, 1), times = c(120, 10, 20))),
stringsAsFactors = TRUE)
MultiCons(parts, Clust_entry = TRUE, Plot = TRUE)