I'm trying to perform k-means on a dataframe with 69 columns and 1000 rows. First, I need to decide upon the optimal numbers of clusters first with the use of the Davies-Bouldin index. This algorithm requires that the input should be in the form of a matrix, I used this code first:
totalm <- data.matrix(total)
Followed by the following code (Davies-Bouldin index)
clusternumber<-0
max_cluster_number <- 30
#Davies Bouldin algorithm
library(clusterCrit)
smallest <-99999
for(b in 2:max_cluster_number){
a <-99999
for(i in 1:200){
cl <- kmeans(totalm,b)
cl<-as.numeric(cl)
intCriteria(totalm,cl$cluster,c("dav"))
if(intCriteria(totalm,cl$cluster,c("dav"))$davies_bouldin < a){
a <- intCriteria(totalm,cl$cluster,c("dav"))$davies_bouldin }
}
if(a<smallest){
smallest <- a
clusternumber <-b
}
}
print("##clusternumber##")
print(clusternumber)
print("##smallest##")
print(smallest)
I keep on getting this error:(list) object cannot be coerced to type 'double'. How can I solve this?
Reproducable example:
a <- c(0,0,1,0,1,0,0)
b <- c(0,0,1,0,0,0,0)
c <- c(1,1,0,0,0,0,1)
d <- c(1,1,0,0,0,0,0)
total <- cbind(a,b,c,d)
The error is coming from cl<-as.numeric(cl)
. The result of a call to kmeans is an object, which is a list containing various information about the model.
Run ?kmeans
I would also recommend you add nstart = 20
to your kmeans call. k-means clustering is a random process. This will run the algorithm 20 times and find the best fit (ie for each number of centers).
for(b in 2:max_cluster_number){
a <-99999
for(i in 1:200){
cl <- kmeans(totalm,centers = b,nstart = 20)
#cl<-as.numeric(cl)
intCriteria(totalm,cl$cluster,c("dav"))
if(intCriteria(totalm,cl$cluster,c("dav"))$davies_bouldin < a){
a <- intCriteria(totalm,cl$cluster,c("dav"))$davies_bouldin }
}
if(a<smallest){
smallest <- a
clusternumber <-b
}
}
This gave me
[1] "##clusternumber##"
[1] 4
[1] "##smallest##"
[1] 0.138675
(tempoarily changing max clusters to 4 as reproducible data is a small set)
EDIT Integer Error
I was able to reproduce your error using
a <- as.integer(c(0,0,1,0,1,0,0))
b <- as.integer(c(0,0,1,0,0,0,0))
c <- as.integer(c(1,1,0,0,0,0,1))
d <- as.integer(c(1,1,0,0,0,0,0))
totalm <- cbind(a,b,c,d)
So that an integer matrix is created.
I was then able to remove the error by using
storage.mode(totalm) <- "double"
Note that
total <- cbind(a,b,c,d)
totalm <- data.matrix(total)
is unnecessary for the data in this example
> identical(total,totalm)
[1] TRUE
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