clusterv (version 1.1.1)

Norm.hclustering: Multiple Hierarchical clusterings using Normal random projections

Description

Multiple Hierarchical clusterings using Normal random projections of the data.

Usage

Norm.hclustering(M, dim, c = 3, hmethod = "average", n = 50, 
                 scale = TRUE, seed = 100, distance="euclidean")
Norm.hclustering.tree(M, dim, hmethod = "average", n = 50, scale = TRUE, 
                      seed = 100, distance = "euclidean")

Value

a list with components "cluster" and "tree":

cluster

list of the n clusterings obtained. Each element is in turn a list of vectors that correspond to the clusters of the clustering. Each cluster is represented by a vector of integers whose values corresponds to the indices of the columns (examples) of the original data.

tree

list of the trees generated by the multiple clusterings

Norm.hclustering.tree returns only the list of the trees.

Arguments

M

matrix of data: rows are variables and columns are examples

dim

subspace dimension

c

number of clusters

hmethod

the agglomeration method to be used. This should be one of "ward.D", "single", "complete", "average", "mcquitty", "median" or "centroid", according to the hclust method of the package stats.

n

number of random projections

scale

if TRUE (default) Normal random projections are scaled

seed

numerical seed for the random generator

distance

it must be one of the two: "euclidean" (default) or "pearson" (that is 1 - Pearson correlation)

Author

Giorgio Valentini

See Also

norm.random.projection

Examples

# \donttest{
# 20 hierarchical clusterings on multiple Normal projected data 
# with subspace dimension equal to 100
M <- generate.sample0(n=10, m=2, sigma=1, dim=800)
l <- Norm.hclustering(M, dim=100, hmethod = "average", n = 20, scale = TRUE)
# Equal as above, but only the trees are generated
l <- Norm.hclustering.tree(M, dim=100, hmethod = "average", n = 20, scale = TRUE)
# 10 hierarchical clusterings on multiple Normal projected data 
# with subspace dimension equal to 200
M <- generate.sample0(n=8, m=1, sigma=2, dim=1000)
l <- Norm.hclustering(M, dim=200, hmethod = "average", n = 10, scale = TRUE)
# }