  Atom probe tomography is commonly used to study solute clustering and
precipitation in materials. However, standard techniques, such as the density
based spatial clustering applications with noise (DBSCAN) perform poorly with
respect to small clusters of less than 25 atoms. This is a fundamental
limitation of density-based clustering techniques due to the usage of Nmin, an
arbitrary lower limit placed on cluster sizes. Therefore, this paper attempts
to consider atom probe clustering as an outlier detection problem of which KNN,
LOF, LUNAR algorithms were tested against a simulated dataset and compared to
the standard method, for a range of cluster sizes. The decision score output of
the algorithms was then auto thresholded by the Karcher mean to remove human
bias. Each of the major models tested outperforms DBSCAN for cluster sizes of
less than 25 atoms but underperforms for sizes greater than 30 atoms. However,
the new combined kNN and DBSCAN method presented was able to perform well at
all cluster sizes. The combined kNN and DBSCAN method is presented as a
possible new standard approach to identifying solute clusters in atom probe
tomography.
