Depth Deviation Score: A New Outlier Detection Framework

An outlier is a data point that differs significantly from the rest of the data. Outliers are also referred to as abnormalities, discordant observations, deviants, or anomalies in the data mining and statistics literature. Detecting outliers is very useful for improving data quality, as outliers can influence the results of data min-ing techniques such as clustering, classification, and association. This paper proposes a simple method for detecting outliers in a dataset based on data depth. Extensive evaluation experiments conducted on real-world datasets, comparing the proposed method with popular outlier detection algorithms, show that its per-formance is competitive and promising.

Data depth; Local outlier factor; Outlier detection

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