I am using the HDBSCAN algorithm so as to perform unsupervised clustering and detect outliers. Based on the documentation there are two outputs from the clustering process that can give insight on which points are outliers.
- The GLOSH outlier detection algorithm that gives a degree of certainty of whether a point is an outlier or not.
- The HDBSCAN labels that if an element in not part of a cluster is considered as noise and has the corresponding label.
I have been working on some data, and I have noticed that these two approaches do not give the same results. That means that there are points in my dataset that are labeled as not part of a cluster but are not detected by the GLOSH outlier algorithm. Should I consider the union of these two approaches as my outliers or I am missing something in their interpretation?