lab <- liste
predicted
<- kmeans(lab
[,
c(2,
3,
4)],
10,nstart
= 20)
colnames(tahmin
) <- c("isim",
"lab1",
"lab2",
"lab3",
"Data",
"Class")
## dbscan
cl <- hdbscan(U, minPts = 5)
#plot outlier scores
plot(sort(cl$outlier_scores
), ylab
="hdbscan score") legend("bottomright",
legend = c("90 percentile",
"95 Percentile"), lty
=1,
col=c("blue",
"red") , bty
= "n")
#find which rows are outlier
which(cl$outlier_scores
>quantile
(cl$outlier_scores,
0.95))
Replies to Re: Untitled 
| Title |
Name |
Language |
UNIX |
When |
| Re: Re: Untitled |
Funky Pheasant |
rsplus |
1617297665 |
3 Years ago. |
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