Description Usage Arguments Value Examples
Performs MultiCons clustering, from Al-Najdi et Al.
For some reason, if you want to use mclust()
clustering, the package
needs to be loaded manually
1 2 3 4 5 6 7 8 9 10 11 12 |
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.
1 2 3 4 5 6 7 8 9 10 11 12 | 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)
|
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