Abstract
In this paper we introduce the concept of singular outliers and provide an algorithm (SODA) for detecting these outliers. Singular outliers are multivariate outliers that differ from conventional outliers by the fact that the anomalous values occur for only one feature (or a relatively small number of features). Singular outliers occur naturally in the fields of fraud detection and data quality, but can be observed in other application fields as well. The SODA algorithm is based on the local Euclidean Manhattan Ratio (LEMR). The algorithm is applied to five real-world data sets and the outliers found by it are qualitatively and quantitatively compared to outliers found by three conventional outlier detection algorithms, showing the different nature of singular outliers.
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Pijnenburg, M., Kowalczyk, W. (2018). Singular Outliers: Finding Common Observations with an Uncommon Feature. In: Medina, J., Ojeda-Aciego, M., Verdegay, J., Perfilieva, I., Bouchon-Meunier, B., Yager, R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. IPMU 2018. Communications in Computer and Information Science, vol 855. Springer, Cham. https://doi.org/10.1007/978-3-319-91479-4_41
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DOI: https://doi.org/10.1007/978-3-319-91479-4_41
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