Abstract
Many outlier detection methods do not merely provide the decision for a single data object being or not being an outlier. Instead, many approaches give an “outlier score” or “outlier factor” indicating “how much” the respective data object is an outlier. Such outlier scores differ widely in their range, contrast, and expressiveness between different outlier models. Even for one and the same outlier model, the same score can indicate a different degree of “outlierness” in different data sets or regions of different characteristics in one data set. Here, we demonstrate a visualization tool based on a unification of outlier scores that allows to compare and evaluate outlier scores visually even for high dimensional data.
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Achtert, E., Kriegel, HP., Reichert, L., Schubert, E., Wojdanowski, R., Zimek, A. (2010). Visual Evaluation of Outlier Detection Models. In: Kitagawa, H., Ishikawa, Y., Li, Q., Watanabe, C. (eds) Database Systems for Advanced Applications. DASFAA 2010. Lecture Notes in Computer Science, vol 5982. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12098-5_34
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DOI: https://doi.org/10.1007/978-3-642-12098-5_34
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