PEERJ COMPUTER SCIENCE, no.., pp.1-22, 2022 (SCI-Expanded)
Outliers are data points that significantly deviate from other data
points in a data set because of different mechanisms or unusual
processes. Outlier detection is one of the intensively studied research
topics for identification of novelties, frauds, anomalies, deviations or
exceptions in addition to its use for data cleansing in data science.
In this study, we propose two novel outlier detection approaches using
the typicality degrees which are the partitioning result of unsupervised
possibilistic clustering algorithms. The proposed approaches are based
on finding the atypical data points below a predefined threshold value, a
possibilistic level for evaluating a point as an outlier. The
experiments on the synthetic and real data sets showed that the proposed
approaches can be successfully used to detect outliers without
considering the structure and distribution of the features in
multidimensional data sets.