  Outlier generation is a popular technique used for solving important outlier
detection tasks. Generating outliers with realistic behavior is challenging.
Popular existing methods tend to disregard the 'multiple views' property of
outliers in high-dimensional spaces. The only existing method accounting for
this property falls short in efficiency and effectiveness. We propose BISECT, a
new outlier generation method that creates realistic outliers mimicking said
property. To do so, BISECT employs a novel proposition introduced in this
article stating how to efficiently generate said realistic outliers. Our method
has better guarantees and complexity than the current methodology for
recreating 'multiple views'. We use the synthetic outliers generated by BISECT
to effectively enhance outlier detection in diverse datasets, for multiple use
cases. For instance, oversampling with BISECT reduced the error by up to 3
times when compared with the baselines.
