Compute Global-Local Outlier Score from Hierarchies. This is based
on hierarchical clustering where the minimum cluster size is k. The resulting
outlier score is a measure of how anomalous each observation is.
The function uses dbscan::hdbscan to do the calculation.
Arguments
- y
Numerical matrix or vector of data
- k
Minimum cluster size. Default: 5.
- ...
Additional arguments passed to
dbscan::hdbscan
See also
dbscan::glosh
Examples
y <- c(rnorm(49), 5)
glosh_scores(y)
#> [1] 0.12699448 0.64273013 0.79844228 0.43444519 0.00000000 0.23366298
#> [7] 0.46290261 0.25321690 0.14141241 0.20984452 0.14187241 0.00000000
#> [13] 0.45795231 0.19232766 0.26695668 0.82081973 0.45947376 0.09156258
#> [19] 0.29104784 0.28875389 0.68625231 0.09156258 0.84897011 0.23550826
#> [25] 0.25008251 0.17631255 0.22665993 0.07367014 0.23550826 0.72740468
#> [31] 0.10125935 0.17173028 0.35438028 0.09667300 0.17984478 0.61503752
#> [37] 0.64249689 0.14865598 0.36906599 0.17173028 0.36206306 0.19048791
#> [43] 0.23366298 0.00000000 0.65192809 0.40074599 0.00000000 0.21549002
#> [49] 0.18766398 0.94880290