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Outlier detection is one of the most important issues in contemporary data analysis. At present,
many methods are employed for anomaly and outlier detection, but there is still no universal
tool that delivers a high degree of efficiency. In this study, we present a novel approach for
outlier detection based on the skillful use of the law of large numbers. The main idea of the
proposed solution consists of the random clustering of the elements of the analyzed set. Then,
those elements that are sufficiently distant from the random cluster centers are marked as outliers.
The proposed approach, besides being highly effective, is also very intuitive. The results of
the conducted numerical experiments confirm the high degree of effectiveness of the proposed
method, with the measures of accuracy and precision reaching a value of 1. The indisputable
advantages of this novel approach for outlier detection are the simplicity of interpretation and the
possibility of its modification by people who may lack an extensive experience in data analysis.
The effectiveness of the proposed method was compared with other recognized techniques in
detecting outliers within both artificially generated and empirical data sets
