  Outlier detection is an important topic in machine learning and has been used
in a wide range of applications. Outliers are objects that are few in number
and deviate from the majority of objects. As a result of these two properties,
we show that outliers are susceptible to a mechanism called fluctuation. This
article proposes a method called fluctuation-based outlier detection (FBOD)
that achieves a low linear time complexity and detects outliers purely based on
the concept of fluctuation without employing any distance, density or isolation
measure. Fundamentally different from all existing methods. FBOD first converts
the Euclidean structure datasets into graphs by using random links, then
propagates the feature value according to the connection of the graph. Finally,
by comparing the difference between the fluctuation of an object and its
neighbors, FBOD determines the object with a larger difference as an outlier.
The results of experiments comparing FBOD with seven state-of-the-art
algorithms on eight real-world tabular datasets and three video datasets show
that FBOD outperforms its competitors in the majority of cases and that FBOD
has only 5% of the execution time of the fastest algorithm. The experiment
codes are available at:
https://github.com/FluctuationOD/Fluctuation-based-Outlier-Detection.
