I used my training dataset to fit cluster using kmenas function
fit <- kmeans(ca.data, 2);
How can I use fit object to predict cluster membership in a new dataset?
Thanks
K-Means Clustering in R – Predicting Cluster for New Object
clustering
Related Solutions
In theory if you know the medoids from the train clustering, you just need to calculate the distances to these medoids again in your test data, and assign it to the closest. So below I use the iris example:
library(cluster)
set.seed(111)
idx = sample(nrow(iris),100)
trn = iris[idx,]
test = iris[-idx,]
mdl = pam(daisy(iris[idx,],metric="gower"),3)
we get out the medoids like this:
trn[mdl$id.med,]
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
40 5.1 3.4 1.5 0.2 setosa
100 5.7 2.8 4.1 1.3 versicolor
113 6.8 3.0 5.5 2.1 virginica
So below I write a function to take these 3 medoid rows out of the train data, calculate a distance matrix with the test data, and extract for each test data, the closest medoid:
predict_pam = function(model,traindata,newdata){
nclus = length(model$id.med)
DM = daisy(rbind(traindata[model$id.med,],newdata),metric="gower")
max.col(-as.matrix(DM)[-c(1:nclus),1:nclus])
}
You can see it works pretty well:
predict_pam(mdl,trn,test)
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3
[39] 3 3 3 3 3 3 3 3 3 3 3 3
We can visualize this:
library(MASS)
library(ggplot2)
df = data.frame(cmdscale(daisy(rbind(trn,test),metric="gower")),
rep(c("train","test"),c(nrow(trn),nrow(test))))
colnames(df) = c("Dim1","Dim2","data")
df$cluster = c(mdl$clustering,predict_pam(mdl,trn,test))
df$cluster = factor(df$cluster)
ggplot(df,aes(x=Dim1,y=Dim2,col=cluster,shape=data)) +
geom_point() + facet_wrap(~data)
Best Answer
One of your options is to use cl_predict from the
cluepackage (note: I found this through googling "kmeans R predict").