Description Usage Arguments Value Examples
Obtain aggreagted GLOSH outlier scores based on hdbscan
1 2 | outlier_hdbscan(mat, k, sampleSize, nEpochs, distMethod = "euclidean",
seed = 1, nproc = 1, distFunc)
|
mat |
(numeric matrix) data matrix |
k |
(pos int) Minimum size of clusters for hdbscan |
sampleSize |
(pos int) Size of the sample |
nEpochs |
(pos int) Number of samples |
distMethod |
(string) Method of compute distance matrix. Default is 'euclidean' |
seed |
(pos int) seed |
nproc |
(pos int) Number of parallel processses to use via forking |
distFunc |
'fun' argument for 'parallelDist::parDist' when distMethod is "custom" |
A vector of outlier scores
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | set.seed(1)
mix3Gaus <- rbind(
mvtnorm::rmvnorm(1e3, mean = c(10, 20))
, mvtnorm::rmvnorm(
2e3
, mean = c(20, 30)
, sigma = matrix(c(1, 0.2, 0.2, 1), ncol = 2))
, mvtnorm::rmvnorm(100, mean = c(15, 25), sigma = diag(6, 2))
)
mix3Gaus <- mix3Gaus[sample(nrow(mix3Gaus)), ]
outScore <- outlier_hdbscan(mat = mix3Gaus
, k = 100
, sampleSize = 1e3
, nEpochs = 1e2
)
plot(density(outScore))
plot(mix3Gaus)
plot(mix3Gaus, col = ifelse(outScore > 0.8, 1, 2))
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.