HDBSCAN Outlier Detection and labeling
GLOSH works with regard to local outliers, thus if a region is very dense and then has a few sparse points near it GLOSH will rate those sparse points as more outlying than other noise points in sparser regions. This is because it is considered particularly odd for a point to be near, but not in a very dense region -- local to the region the point is a significant outlier.

With regard to your question: if you care about local outliers (things that are odd relative to the density of the nearby region) then GLOSH is a good choice. This would be useful, for example, in detecting network intrusions since an attacker will be trying to behave like normal users, but will be slightly outside the dense normal user cluster. If, on the other hand, you are more interested in generally outlying points then merely selecting the noise points from the clustering will pick out points in sparser regions of the space.

Finally note that selecting noise points gives you only a single threshold; in contract GLOSH provides a range of scores and you can tune the threshold to find the number of outliers you desire.