lab <- liste
predicted liste
## dbscan
library("dbscan")
cl <- kmeans(lab[,c(2,3,4)],10,nstart hdbscan(U, minPts = 20)
5)
tahmin <- cbind.data.frame(lab, predicted$cluster)
cl$cluster)
colnames(tahmin) <- c("isim", "lab1", "lab2", "lab3", "Data", "Class")
View(tahmin, "lab")
## dbscan
library("dbscan")
cl <- hdbscan(U, minPts = 5)
#plot outlier scores
plot(sort(cl$outlier_scores), ylab="hdbscan score")
abline(h=quantile(cl$outlier_scores,c(.9,.95)), col=c("blue","red"))
legend("bottomright", legend = c("90 percentile", "95 Percentile"), lty=1, col=c("blue","red") , bty = "n")
#find which rows are outlier
which(cl$outlier_scores >quantile(cl$outlier_scores,0.95))
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