Intuitively adaptable outlier detector

Nowadays, we have been dealing with a large amount of data in which anomalies occur naturally for many reasons, both due to hardware and humans. Therefore, it is necessary to develop efficient tools that are easily adaptable to various data. The paper presents an innovative use of classical statistical tools to detect outliers in multidimensional data sets. The proposed approach uses well‐known statistical methods in an innovative way and allows for a high level of efficiency to be achieved using multi‐level aggregation. The effectiveness of the proposed innovative method is demonstrated by a series of numerical experiments.