Fuzzy C-Means Clustering with clValid Function
I am using the function clValid from the clValid package and validating my fuzzy clusters with the fanny argument.
intvalid <- clValid(clust, 2:10, clMethods=c("fanny"),
validation="internal", metric='euclidean', maxitems = 1000)
However, I would like to test the validity using SqEuclidean, hence use Fuzzy C-Means Clustering rather than Fuzzy Clustering. I know that the fanny algorithm in clValid is from the cluster package. I know the only three options are euclidean, correlation, and manhattan, hence there is not a way to set the distance to SqEuclidean.
How do I perform clValid with Fuzzy C-Means Clustering? Or am I misinterpreting the metric argument of the fanny function in clValid?
character string specifying the metric to be used for calculating dissimilarities between observations. Options are "euclidean" (default), "manhattan", and "SqEuclidean". Euclidean distances are root sum-of-squares of differences, and manhattan distances are the sum of absolute differences, and "SqEuclidean", the squared euclidean distances are sum-of-squares of differences. Using this last option is equivalent (but somewhat slower) to computing so called “fuzzy C-means”. If x is already a dissimilarity matrix, then this argument will be ignored.
Data
library(dplyr)
library(cluster)
library(clValid)
df<-iris[,-5] # I do not use iris, but to make reproducible
clust<-sapply(df,scale)
cluster-analysis fuzzy
asked Nov 19 '18 at 16:30
Jack ArmstrongJack Armstrong
318619
add a comment |
I am using the function clValid from the clValid package and validating my fuzzy clusters with the fanny argument.
intvalid <- clValid(clust, 2:10, clMethods=c("fanny"),
validation="internal", metric='euclidean', maxitems = 1000)
However, I would like to test the validity using SqEuclidean, hence use Fuzzy C-Means Clustering rather than Fuzzy Clustering. I know that the fanny algorithm in clValid is from the cluster package. I know the only three options are euclidean, correlation, and manhattan, hence there is not a way to set the distance to SqEuclidean.
How do I perform clValid with Fuzzy C-Means Clustering? Or am I misinterpreting the metric argument of the fanny function in clValid?
character string specifying the metric to be used for calculating dissimilarities between observations. Options are "euclidean" (default), "manhattan", and "SqEuclidean". Euclidean distances are root sum-of-squares of differences, and manhattan distances are the sum of absolute differences, and "SqEuclidean", the squared euclidean distances are sum-of-squares of differences. Using this last option is equivalent (but somewhat slower) to computing so called “fuzzy C-means”. If x is already a dissimilarity matrix, then this argument will be ignored.
Data
library(dplyr)
library(cluster)
library(clValid)
df<-iris[,-5] # I do not use iris, but to make reproducible
clust<-sapply(df,scale)
cluster-analysis fuzzy
asked Nov 19 '18 at 16:30
Jack ArmstrongJack Armstrong
318619
add a comment |
I am using the function clValid from the clValid package and validating my fuzzy clusters with the fanny argument.
intvalid <- clValid(clust, 2:10, clMethods=c("fanny"),
validation="internal", metric='euclidean', maxitems = 1000)
However, I would like to test the validity using SqEuclidean, hence use Fuzzy C-Means Clustering rather than Fuzzy Clustering. I know that the fanny algorithm in clValid is from the cluster package. I know the only three options are euclidean, correlation, and manhattan, hence there is not a way to set the distance to SqEuclidean.
How do I perform clValid with Fuzzy C-Means Clustering? Or am I misinterpreting the metric argument of the fanny function in clValid?
character string specifying the metric to be used for calculating dissimilarities between observations. Options are "euclidean" (default), "manhattan", and "SqEuclidean". Euclidean distances are root sum-of-squares of differences, and manhattan distances are the sum of absolute differences, and "SqEuclidean", the squared euclidean distances are sum-of-squares of differences. Using this last option is equivalent (but somewhat slower) to computing so called “fuzzy C-means”. If x is already a dissimilarity matrix, then this argument will be ignored.
Data
library(dplyr)
library(cluster)
library(clValid)
df<-iris[,-5] # I do not use iris, but to make reproducible
clust<-sapply(df,scale)
cluster-analysis fuzzy
asked Nov 19 '18 at 16:30
Jack ArmstrongJack Armstrong
318619
I am using the function clValid from the clValid package and validating my fuzzy clusters with the fanny argument.
intvalid <- clValid(clust, 2:10, clMethods=c("fanny"),
validation="internal", metric='euclidean', maxitems = 1000)
However, I would like to test the validity using SqEuclidean, hence use Fuzzy C-Means Clustering rather than Fuzzy Clustering. I know that the fanny algorithm in clValid is from the cluster package. I know the only three options are euclidean, correlation, and manhattan, hence there is not a way to set the distance to SqEuclidean.
How do I perform clValid with Fuzzy C-Means Clustering? Or am I misinterpreting the metric argument of the fanny function in clValid?
character string specifying the metric to be used for calculating dissimilarities between observations. Options are "euclidean" (default), "manhattan", and "SqEuclidean". Euclidean distances are root sum-of-squares of differences, and manhattan distances are the sum of absolute differences, and "SqEuclidean", the squared euclidean distances are sum-of-squares of differences. Using this last option is equivalent (but somewhat slower) to computing so called “fuzzy C-means”. If x is already a dissimilarity matrix, then this argument will be ignored.
Data
library(dplyr)
library(cluster)
library(clValid)
df<-iris[,-5] # I do not use iris, but to make reproducible
clust<-sapply(df,scale)
cluster-analysis fuzzy
cluster-analysis fuzzy
asked Nov 19 '18 at 16:30
Jack ArmstrongJack Armstrong
318619
asked Nov 19 '18 at 16:30
Jack ArmstrongJack Armstrong
318619
edited Nov 19 '18 at 17:04
Jack Armstrong
asked Nov 19 '18 at 16:30
Jack ArmstrongJack Armstrong
318619
asked Nov 19 '18 at 16:30
Jack ArmstrongJack Armstrong
318619
asked Nov 19 '18 at 16:30
Jack ArmstrongJack Armstrong
318619
318619
add a comment |
add a comment |
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