Nick Knauer - 1 year ago 123

R Question

I have two variables that I calculated from Matrix B:

1) The Correlation Matrix

`cor(B)`

2) The Hierarchical Cluster of the Dissimilarity Matrix from the Correlation Matrix

I then used the

`clustConfigurations`

See Code Below:

`library(NetCluster)`

B = matrix(

c(2, 0, 0, 1, 0, 0, 1,

0, 1, 0, 0, 2, 1, 0,

0, 0, 3, 1, 0, 0, 2,

1, 0, 1, 4, 0, 0, 2,

0, 0, 0, 0, 4, 0, 2,

0, 1, 0, 0, 0, 2, 1,

1, 0, 2, 2, 2, 1, 8),

nrow=7,

ncol=7)

colnames(B) = c("A", "B", "C", "D", "E", "F", "G")

rownames(B) = c("A", "B", "C", "D", "E", "F", "G")

B

A B C D E F G

A 2 0 0 1 0 0 1

B 0 1 0 0 0 1 0

C 0 0 3 1 0 0 2

D 1 0 1 4 0 0 2

E 0 2 0 0 4 0 2

F 0 1 0 0 0 2 1

G 1 0 2 2 2 1 8

Correlation_Matrix <- cor(B)

dissimilarity <- 1 - Correlation_Matrix

Correlation_Matrix_dist <- as.dist(dissimilarity)

Correlation_Matrix_dist

HClust_Correlation_Matrix <- hclust(Correlation_Matrix_dist)

clustered_observed_cors = vector()

num_vertices <- ncol(B)

clustered_observed_cors1 <-clustConfigurations(num_vertices,HClust_Correlation_Matrix,Correlation_Matrix)

When I tried doing this with a larger matrix specifically 1213 x 1213, the matrix was too large to run this script so I decided to use another package called

`NbClust`

Documentation:

https://cran.r-project.org/web/packages/NbClust/NbClust.pdf

My goal was to recreate the process above with this new package but I'm not sure whether or not the below code is equivalent to above:

`library(NbClust)`

nbclustering<-NbClust(diss = Correlation_Matrix_dist,

distance = NULL,

min.nc=2,

max.nc=20,

method = "complete",

index = "dunn")

This would give you the optimal amount of clusters:

nbclustering$Best.nc

Is the above code equivalent to my original code and if not, what change do I need to make?

Thanks!

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Answer Source

`NbClust`

is a broader function than `hclust`

with more focus on the metrics to assess the final number of clusters.

The default method for `hclust`

is `"complete"`

.

It is the same method used with NbClust with the option: `method = "complete"`

.

So it is correct to use the result of Nbclust to define the number of final cluster for the clustering obtained by the function `hclust`

.