  Over the decades, traditional outlier detectors have ignored the group-level
factor when calculating outlier scores for objects in data by evaluating only
the object-level factor, failing to capture the collective outliers. To
mitigate this issue, we present a method called neighborhood representative
(NR), which empowers all the existing outlier detectors to efficiently detect
outliers, including collective outliers, while maintaining their computational
integrity. It achieves this by selecting representative objects, scoring these
objects, then applies the score of the representative objects to its collective
objects. Without altering existing detectors, NR is compatible with existing
detectors, while improving performance on real world datasets with +8% (0.72 to
0.78 AUC) relative to state-of-the-art outlier detectors.
