And in case you would like to explore the supervised clustering approach, I may
suggest to explore the
use of knn() fed by a training set determined by your cluster assignments
expectations.
Some "quick code" to show what I mean.
z <- as.data.frame(cbind(scale(x), scale(y)))
colnames(z) <- c("x", "y")
n <- nrow(z)
train <- seq(1,0.3*n,1)
ztrain <- as.data.frame(z[train,])
cl <- vector(mode="numeric", length=length(train))
for (i in 1:nrow(ztrain)) {
if (ztrain[i,"y"] > 2 | ztrain[i,"x"] > 0) {
cl[i] <- 2
} else {
cl[i] <- 1
}
}
plot(ztrain, col=cl)
library(class)
ztest<- as.data.frame(z[-train,])
knn.model <- knn(ztrain, ztest, cl, k = 3)
plot(ztest, col=knn.model)
ztrain$cl <- cl
ztest$cl <- knn.model
z.res <- rbind(ztrain,ztest)
plot(z.res$x, z.res$y, col=z.res$cl)
--
GG
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