Abstract
“An outlier is an observation which deviates so much from the other observations as to arouse suspicions that it was generated by a different mechanism.”
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- 1.
Note that higher \(k\)-nearest neighbor distances indicate greater outlierness.
- 2.
We say “almost,” because the very last distance computation for \(\overline {X}\) may bring \(V(\overline {X})\) below \(L\). This scenario is unusual, but might occasionally occur.
- 3.
Most descriptions in the literature omit the first phase of sampling, which is very important for efficiency maximization. A number of implementations in time-series analysis [306] do order the data points more carefully but not with sampling.
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Aggarwal, C. (2015). Outlier Analysis. In: Data Mining. Springer, Cham. https://doi.org/10.1007/978-3-319-14142-8_8
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DOI: https://doi.org/10.1007/978-3-319-14142-8_8
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