  The neighbor-based method has become a powerful tool to handle the outlier
detection problem, which aims to infer the abnormal degree of the sample based
on the compactness of the sample and its neighbors. However, the existing
methods commonly focus on designing different processes to locate outliers in
the dataset, while the contributions of different types neighbors to outlier
detection has not been well discussed. To this end, this paper studies the
neighbor in the existing outlier detection algorithms and a taxonomy is
introduced, which uses the three-level components of information, neighbor and
methodology to define hybrid methods. This taxonomy can serve as a paradigm
where a novel neighbor-based outlier detection method can be proposed by
combining different components in this taxonomy. A large number of comparative
experiments were conducted on synthetic and real-world datasets in terms of
performance comparison and case study, and the results show that reverse
K-nearest neighbor based methods achieve promising performance and dynamic
selection method is suitable for working in high-dimensional space. Notably, it
is verified that rationally selecting components from this taxonomy may create
an algorithms superior to existing methods.
