  How can we detect outliers, both scattered and clustered, and also explicitly
assign them to respective micro-clusters, without knowing apriori how many
micro-clusters exist? How can we perform both tasks in-house, i.e., without any
post-hoc processing, so that both detection and assignment can benefit
simultaneously from each other? Presenting outliers in separate micro-clusters
is informative to analysts in many real-world applications. However, a na\"ive
solution based on post-hoc clustering of the outliers detected by any existing
method suffers from two main drawbacks: (a) appropriate hyperparameter values
are commonly unknown for clustering, and most algorithms struggle with clusters
of varying shapes and densities; (b) detection and assignment cannot benefit
from one another. In this paper, we propose D.MCA to $\underline{D}$etect
outliers with explicit $\underline{M}$icro-$\underline{C}$luster
$\underline{A}$ssignment. Our method performs both detection and assignment
iteratively, and in-house, by using a novel strategy that prunes entire
micro-clusters out of the training set to improve the performance of the
detection. It also benefits from a novel strategy that avoids clustered
outliers to mask each other, which is a well-known problem in the literature.
Also, D.MCA is designed to be robust to a critical hyperparameter by employing
a hyperensemble "warm up" phase. Experiments performed on 16 real-world and
synthetic datasets demonstrate that D.MCA outperforms 8 state-of-the-art
competitors, especially on the explicit outlier micro-cluster assignment task.
