The initialization is based on Coretto and Hennig (2017). First, wwo
steps are performed:
Step 1 (denoising step): for each data point compute its
k
th-
nearest neighbors
distance (k-
NND). All points with k-
NND larger
than the (1-knnd.trim
)-
quantile of the k-
NND
are initialized as noise. Intepretaion of
k
is that: (k-1)
, but not k
, points close
together may still be interpreted as noise or outliers
Step 2 (clustering step): perform the model-based hierarchical
clustering (MBHC) proposed in Fraley (1998). This step is performed using
hc
. The input argument modelName
is passed
to hc
. See Details of
hc
for more details.
If the previous Step 2 fails to provide G
clusters each
containing at least 2 distinct data points, it is replaced with
classical hirararchical clustering implemented in
hclust
. Finally, if
hclust
fails to provide a valid partition, up
to ten random partitions are tried.