R cutree on dendrogram with “centroid” and “median” methods yields more clusters than expected
I want to test the hierarchical clustering with "centroid" and "median" methods. I have the following R code:
library(dendextend)
iris <- datasets::iris
iris2 <- iris[,-5]
species_labels <- iris[,5]
d_iris <- dist(iris2)
hc_iris <- hclust(d_iris, method = "centroid")
dend <- as.dendrogram(hc_iris)
dend <- color_branches(dend, k=3)
plot(dend,
main = "Clustered Iris data set
(the labels give the true flower species)",
horiz = TRUE, nodePar = list(cex = .007))
The number of clusters seems to be larger than the k in the color_branches function.
However, if I do the cutree directly on hc_iris, which is the result of hierarchical clustering:
table(cutree(hc_iris, k=3), iris$Species)
I get 3 clusters, as expected:
setosa versicolor virginica
50 0 0
0 50 48
0 0 2
But if I apply the cutree function on the dendrogram, the number of clusters is 34:
table(cutree(as.dendrogram(hc_iris), 3), iris$Species)
setosa versicolor virginica
4 0 0
3 0 0
3 0 0
6 0 0
2 0 0
3 0 0
10 0 0
5 0 0
4 0 0
1 0 0
1 0 0
2 0 0
1 0 0
3 0 0
2 0 0
0 3 0
0 27 9
0 12 0
0 2 0
0 3 0
0 1 3
0 2 0
0 0 9
0 0 3
0 0 6
0 0 2
0 0 3
0 0 3
0 0 3
0 0 2
0 0 2
0 0 2
0 0 1
0 0 2
This happens with both "centroid" and "median" methods.
r hierarchical-clustering dendrogram dendextend
edited Dec 31 '18 at 13:03
G5W
22.3k92041
asked Dec 31 '18 at 12:58
zaigzaig
1348
add a comment |
I want to test the hierarchical clustering with "centroid" and "median" methods. I have the following R code:
library(dendextend)
iris <- datasets::iris
iris2 <- iris[,-5]
species_labels <- iris[,5]
d_iris <- dist(iris2)
hc_iris <- hclust(d_iris, method = "centroid")
dend <- as.dendrogram(hc_iris)
dend <- color_branches(dend, k=3)
plot(dend,
main = "Clustered Iris data set
(the labels give the true flower species)",
horiz = TRUE, nodePar = list(cex = .007))
The number of clusters seems to be larger than the k in the color_branches function.
However, if I do the cutree directly on hc_iris, which is the result of hierarchical clustering:
table(cutree(hc_iris, k=3), iris$Species)
I get 3 clusters, as expected:
setosa versicolor virginica
50 0 0
0 50 48
0 0 2
But if I apply the cutree function on the dendrogram, the number of clusters is 34:
table(cutree(as.dendrogram(hc_iris), 3), iris$Species)
setosa versicolor virginica
4 0 0
3 0 0
3 0 0
6 0 0
2 0 0
3 0 0
10 0 0
5 0 0
4 0 0
1 0 0
1 0 0
2 0 0
1 0 0
3 0 0
2 0 0
0 3 0
0 27 9
0 12 0
0 2 0
0 3 0
0 1 3
0 2 0
0 0 9
0 0 3
0 0 6
0 0 2
0 0 3
0 0 3
0 0 3
0 0 2
0 0 2
0 0 2
0 0 1
0 0 2
This happens with both "centroid" and "median" methods.
r hierarchical-clustering dendrogram dendextend
edited Dec 31 '18 at 13:03
G5W
22.3k92041
asked Dec 31 '18 at 12:58
zaigzaig
1348
1
I think what you are doing should work. Bug? but a work-around is this. Change your color_branches statement todend <- color_branches(dend, clusters=cutree(hc_iris,3))
– G5W
Dec 31 '18 at 13:20
add a comment |
I want to test the hierarchical clustering with "centroid" and "median" methods. I have the following R code:
library(dendextend)
iris <- datasets::iris
iris2 <- iris[,-5]
species_labels <- iris[,5]
d_iris <- dist(iris2)
hc_iris <- hclust(d_iris, method = "centroid")
dend <- as.dendrogram(hc_iris)
dend <- color_branches(dend, k=3)
plot(dend,
main = "Clustered Iris data set
(the labels give the true flower species)",
horiz = TRUE, nodePar = list(cex = .007))
The number of clusters seems to be larger than the k in the color_branches function.
However, if I do the cutree directly on hc_iris, which is the result of hierarchical clustering:
table(cutree(hc_iris, k=3), iris$Species)
I get 3 clusters, as expected:
setosa versicolor virginica
50 0 0
0 50 48
0 0 2
But if I apply the cutree function on the dendrogram, the number of clusters is 34:
table(cutree(as.dendrogram(hc_iris), 3), iris$Species)
setosa versicolor virginica
4 0 0
3 0 0
3 0 0
6 0 0
2 0 0
3 0 0
10 0 0
5 0 0
4 0 0
1 0 0
1 0 0
2 0 0
1 0 0
3 0 0
2 0 0
0 3 0
0 27 9
0 12 0
0 2 0
0 3 0
0 1 3
0 2 0
0 0 9
0 0 3
0 0 6
0 0 2
0 0 3
0 0 3
0 0 3
0 0 2
0 0 2
0 0 2
0 0 1
0 0 2
This happens with both "centroid" and "median" methods.
r hierarchical-clustering dendrogram dendextend
edited Dec 31 '18 at 13:03
G5W
22.3k92041
asked Dec 31 '18 at 12:58
zaigzaig
1348
I want to test the hierarchical clustering with "centroid" and "median" methods. I have the following R code:
library(dendextend)
iris <- datasets::iris
iris2 <- iris[,-5]
species_labels <- iris[,5]
d_iris <- dist(iris2)
hc_iris <- hclust(d_iris, method = "centroid")
dend <- as.dendrogram(hc_iris)
dend <- color_branches(dend, k=3)
plot(dend,
main = "Clustered Iris data set
(the labels give the true flower species)",
horiz = TRUE, nodePar = list(cex = .007))
The number of clusters seems to be larger than the k in the color_branches function.
However, if I do the cutree directly on hc_iris, which is the result of hierarchical clustering:
table(cutree(hc_iris, k=3), iris$Species)
I get 3 clusters, as expected:
setosa versicolor virginica
50 0 0
0 50 48
0 0 2
But if I apply the cutree function on the dendrogram, the number of clusters is 34:
table(cutree(as.dendrogram(hc_iris), 3), iris$Species)
setosa versicolor virginica
4 0 0
3 0 0
3 0 0
6 0 0
2 0 0
3 0 0
10 0 0
5 0 0
4 0 0
1 0 0
1 0 0
2 0 0
1 0 0
3 0 0
2 0 0
0 3 0
0 27 9
0 12 0
0 2 0
0 3 0
0 1 3
0 2 0
0 0 9
0 0 3
0 0 6
0 0 2
0 0 3
0 0 3
0 0 3
0 0 2
0 0 2
0 0 2
0 0 1
0 0 2
This happens with both "centroid" and "median" methods.
r hierarchical-clustering dendrogram dendextend
r hierarchical-clustering dendrogram dendextend
edited Dec 31 '18 at 13:03
G5W
22.3k92041
asked Dec 31 '18 at 12:58
zaigzaig
1348
edited Dec 31 '18 at 13:03
G5W
22.3k92041
asked Dec 31 '18 at 12:58
zaigzaig
1348
edited Dec 31 '18 at 13:03
G5W
22.3k92041
edited Dec 31 '18 at 13:03
G5W
22.3k92041
edited Dec 31 '18 at 13:03
G5W
22.3k92041
22.3k92041
asked Dec 31 '18 at 12:58
zaigzaig
1348
asked Dec 31 '18 at 12:58
zaigzaig
1348
asked Dec 31 '18 at 12:58
zaigzaig
1348
1348
1
I think what you are doing should work. Bug? but a work-around is this. Change your color_branches statement todend <- color_branches(dend, clusters=cutree(hc_iris,3))
– G5W
Dec 31 '18 at 13:20
add a comment |
1
I think what you are doing should work. Bug? but a work-around is this. Change your color_branches statement todend <- color_branches(dend, clusters=cutree(hc_iris,3))
– G5W
Dec 31 '18 at 13:20
1
1
I think what you are doing should work. Bug? but a work-around is this. Change your color_branches statement to
dend <- color_branches(dend, clusters=cutree(hc_iris,3))– G5W
Dec 31 '18 at 13:20
I think what you are doing should work. Bug? but a work-around is this. Change your color_branches statement to
dend <- color_branches(dend, clusters=cutree(hc_iris,3))– G5W
Dec 31 '18 at 13:20
add a comment |
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I think what you are doing should work. Bug? but a work-around is this. Change your color_branches statement to
dend <- color_branches(dend, clusters=cutree(hc_iris,3))– G5W
Dec 31 '18 at 13:20