can pvclust combine not variables,but obs. in R
Let's take it as example
library("MASS")
library("pvclust")
result.par <- pvclust(Boston, nboot=1000, parallel=TRUE)
plot(result.par)
We see that pvclust combines variables.
Is it possible to combine observation in clusters
ie. i want output (with cluster var)
id crim zn indus chas nox rm age dis rad tax ptratio black lstat medv cluster
1 1 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 396.90 4.98 24.0 1
2 2 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 396.90 9.14 21.6 2
3 3 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 392.83 4.03 34.7 1
4 4 0.03237 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 394.63 2.94 33.4 2
5 5 0.06905 0.0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 396.90 5.33 36.2 3
6 6 0.02985 0.0 2.18 0 0.458 6.430 58.7 6.0622 3 222 18.7 394.12 5.21 28.7 3
7 7 0.08829 12.5 7.87 0 0.524 6.012 66.6 5.5605 5 311 15.2 395.60 12.43 22.9 1
8 8 0.14455 12.5 7.87 0 0.524 6.172 96.1 5.9505 5 311 15.2 396.90 19.15 27.1 1
9 9 0.21124 12.5 7.87 0 0.524 5.631 100.0 6.0821 5 311 15.2 386.63 29.93 16.5 2
10 10 0.17004 12.5 7.87 0 0.524 6.004 85.9 6.5921 5 311 15.2 386.71 17.10 18.9 2
how to assing clusters to the obzervations
r pvclust
asked Nov 14 '18 at 19:05
D.JoeD.Joe
6331616
add a comment |
Let's take it as example
library("MASS")
library("pvclust")
result.par <- pvclust(Boston, nboot=1000, parallel=TRUE)
plot(result.par)
We see that pvclust combines variables.
Is it possible to combine observation in clusters
ie. i want output (with cluster var)
id crim zn indus chas nox rm age dis rad tax ptratio black lstat medv cluster
1 1 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 396.90 4.98 24.0 1
2 2 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 396.90 9.14 21.6 2
3 3 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 392.83 4.03 34.7 1
4 4 0.03237 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 394.63 2.94 33.4 2
5 5 0.06905 0.0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 396.90 5.33 36.2 3
6 6 0.02985 0.0 2.18 0 0.458 6.430 58.7 6.0622 3 222 18.7 394.12 5.21 28.7 3
7 7 0.08829 12.5 7.87 0 0.524 6.012 66.6 5.5605 5 311 15.2 395.60 12.43 22.9 1
8 8 0.14455 12.5 7.87 0 0.524 6.172 96.1 5.9505 5 311 15.2 396.90 19.15 27.1 1
9 9 0.21124 12.5 7.87 0 0.524 5.631 100.0 6.0821 5 311 15.2 386.63 29.93 16.5 2
10 10 0.17004 12.5 7.87 0 0.524 6.004 85.9 6.5921 5 311 15.2 386.71 17.10 18.9 2
how to assing clusters to the obzervations
r pvclust
asked Nov 14 '18 at 19:05
D.JoeD.Joe
6331616
add a comment |
Let's take it as example
library("MASS")
library("pvclust")
result.par <- pvclust(Boston, nboot=1000, parallel=TRUE)
plot(result.par)
We see that pvclust combines variables.
Is it possible to combine observation in clusters
ie. i want output (with cluster var)
id crim zn indus chas nox rm age dis rad tax ptratio black lstat medv cluster
1 1 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 396.90 4.98 24.0 1
2 2 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 396.90 9.14 21.6 2
3 3 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 392.83 4.03 34.7 1
4 4 0.03237 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 394.63 2.94 33.4 2
5 5 0.06905 0.0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 396.90 5.33 36.2 3
6 6 0.02985 0.0 2.18 0 0.458 6.430 58.7 6.0622 3 222 18.7 394.12 5.21 28.7 3
7 7 0.08829 12.5 7.87 0 0.524 6.012 66.6 5.5605 5 311 15.2 395.60 12.43 22.9 1
8 8 0.14455 12.5 7.87 0 0.524 6.172 96.1 5.9505 5 311 15.2 396.90 19.15 27.1 1
9 9 0.21124 12.5 7.87 0 0.524 5.631 100.0 6.0821 5 311 15.2 386.63 29.93 16.5 2
10 10 0.17004 12.5 7.87 0 0.524 6.004 85.9 6.5921 5 311 15.2 386.71 17.10 18.9 2
how to assing clusters to the obzervations
r pvclust
asked Nov 14 '18 at 19:05
D.JoeD.Joe
6331616
Let's take it as example
library("MASS")
library("pvclust")
result.par <- pvclust(Boston, nboot=1000, parallel=TRUE)
plot(result.par)
We see that pvclust combines variables.
Is it possible to combine observation in clusters
ie. i want output (with cluster var)
id crim zn indus chas nox rm age dis rad tax ptratio black lstat medv cluster
1 1 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 396.90 4.98 24.0 1
2 2 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 396.90 9.14 21.6 2
3 3 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 392.83 4.03 34.7 1
4 4 0.03237 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 394.63 2.94 33.4 2
5 5 0.06905 0.0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 396.90 5.33 36.2 3
6 6 0.02985 0.0 2.18 0 0.458 6.430 58.7 6.0622 3 222 18.7 394.12 5.21 28.7 3
7 7 0.08829 12.5 7.87 0 0.524 6.012 66.6 5.5605 5 311 15.2 395.60 12.43 22.9 1
8 8 0.14455 12.5 7.87 0 0.524 6.172 96.1 5.9505 5 311 15.2 396.90 19.15 27.1 1
9 9 0.21124 12.5 7.87 0 0.524 5.631 100.0 6.0821 5 311 15.2 386.63 29.93 16.5 2
10 10 0.17004 12.5 7.87 0 0.524 6.004 85.9 6.5921 5 311 15.2 386.71 17.10 18.9 2
how to assing clusters to the obzervations
r pvclust
r pvclust
asked Nov 14 '18 at 19:05
D.JoeD.Joe
6331616
asked Nov 14 '18 at 19:05
D.JoeD.Joe
6331616
asked Nov 14 '18 at 19:05
D.JoeD.Joe
6331616
asked Nov 14 '18 at 19:05
D.JoeD.Joe
6331616
asked Nov 14 '18 at 19:05
D.JoeD.Joe
6331616
6331616
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
Mclust function from mclust package is a valuable option.
library("MASS")
library("mclust")
result.par <- Mclust(Boston)
head(cbind(Boston, cluster=result.par$classification))
https://cran.r-project.org/web/packages/mclust/vignettes/mclust.html
You can also visualize your cluster by removing dendrogram by rows and clustering only features for easiness of visualization. Mclust perform mixture model clustering, so things should change a bit compared to hierarchical clustering approaches.
library(NMF)
aheatmap(as.matrix(Boston_2[,-15]), # remove cluster from data
annRow = as.character(Boston_2[,15]), # consider cluster for annotating rows
Rowv = NA)
answered Nov 15 '18 at 2:27
paoloeusebipaoloeusebi
641414
very good, but how can i draw dendgrogram for these clusters?
– D.Joe
Nov 15 '18 at 10:04
1
I have updated the answer.
– paoloeusebi
Nov 15 '18 at 12:22
add a comment |
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1 Answer
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oldest
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Mclust function from mclust package is a valuable option.
library("MASS")
library("mclust")
result.par <- Mclust(Boston)
head(cbind(Boston, cluster=result.par$classification))
https://cran.r-project.org/web/packages/mclust/vignettes/mclust.html
You can also visualize your cluster by removing dendrogram by rows and clustering only features for easiness of visualization. Mclust perform mixture model clustering, so things should change a bit compared to hierarchical clustering approaches.
library(NMF)
aheatmap(as.matrix(Boston_2[,-15]), # remove cluster from data
annRow = as.character(Boston_2[,15]), # consider cluster for annotating rows
Rowv = NA)
answered Nov 15 '18 at 2:27
paoloeusebipaoloeusebi
641414
very good, but how can i draw dendgrogram for these clusters?
– D.Joe
Nov 15 '18 at 10:04
1
I have updated the answer.
– paoloeusebi
Nov 15 '18 at 12:22
add a comment |
Mclust function from mclust package is a valuable option.
library("MASS")
library("mclust")
result.par <- Mclust(Boston)
head(cbind(Boston, cluster=result.par$classification))
https://cran.r-project.org/web/packages/mclust/vignettes/mclust.html
You can also visualize your cluster by removing dendrogram by rows and clustering only features for easiness of visualization. Mclust perform mixture model clustering, so things should change a bit compared to hierarchical clustering approaches.
library(NMF)
aheatmap(as.matrix(Boston_2[,-15]), # remove cluster from data
annRow = as.character(Boston_2[,15]), # consider cluster for annotating rows
Rowv = NA)
answered Nov 15 '18 at 2:27
paoloeusebipaoloeusebi
641414
very good, but how can i draw dendgrogram for these clusters?
– D.Joe
Nov 15 '18 at 10:04
1
I have updated the answer.
– paoloeusebi
Nov 15 '18 at 12:22
add a comment |
Mclust function from mclust package is a valuable option.
library("MASS")
library("mclust")
result.par <- Mclust(Boston)
head(cbind(Boston, cluster=result.par$classification))
https://cran.r-project.org/web/packages/mclust/vignettes/mclust.html
You can also visualize your cluster by removing dendrogram by rows and clustering only features for easiness of visualization. Mclust perform mixture model clustering, so things should change a bit compared to hierarchical clustering approaches.
library(NMF)
aheatmap(as.matrix(Boston_2[,-15]), # remove cluster from data
annRow = as.character(Boston_2[,15]), # consider cluster for annotating rows
Rowv = NA)
answered Nov 15 '18 at 2:27
paoloeusebipaoloeusebi
641414
Mclust function from mclust package is a valuable option.
library("MASS")
library("mclust")
result.par <- Mclust(Boston)
head(cbind(Boston, cluster=result.par$classification))
https://cran.r-project.org/web/packages/mclust/vignettes/mclust.html
You can also visualize your cluster by removing dendrogram by rows and clustering only features for easiness of visualization. Mclust perform mixture model clustering, so things should change a bit compared to hierarchical clustering approaches.
library(NMF)
aheatmap(as.matrix(Boston_2[,-15]), # remove cluster from data
annRow = as.character(Boston_2[,15]), # consider cluster for annotating rows
Rowv = NA)
answered Nov 15 '18 at 2:27
paoloeusebipaoloeusebi
641414
edited Nov 15 '18 at 12:19
answered Nov 15 '18 at 2:27
paoloeusebipaoloeusebi
641414
answered Nov 15 '18 at 2:27
paoloeusebipaoloeusebi
641414
answered Nov 15 '18 at 2:27
paoloeusebipaoloeusebi
641414
641414
very good, but how can i draw dendgrogram for these clusters?
– D.Joe
Nov 15 '18 at 10:04
1
I have updated the answer.
– paoloeusebi
Nov 15 '18 at 12:22
add a comment |
very good, but how can i draw dendgrogram for these clusters?
– D.Joe
Nov 15 '18 at 10:04
1
I have updated the answer.
– paoloeusebi
Nov 15 '18 at 12:22
very good, but how can i draw dendgrogram for these clusters?
– D.Joe
Nov 15 '18 at 10:04
very good, but how can i draw dendgrogram for these clusters?
– D.Joe
Nov 15 '18 at 10:04
1
1
I have updated the answer.
– paoloeusebi
Nov 15 '18 at 12:22
I have updated the answer.
– paoloeusebi
Nov 15 '18 at 12:22
add a comment |
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