Abstract
Many scenarios for outlier analysis cannot be addressed with the use of the techniques discussed in the previous chapter. For example, the data type has a critical impact on the outlier detection algorithm. In order to use an outlier detection algorithm on categorical data, it may be necessary to change the distance function or the family of distributions used in expectation–maximization (EM) algorithms. In many cases, these changes are exactly analogous to those required in the context of the clustering problem.
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Notes
- 1.
In the case of ranks, if the maximum function is used, then outliers occurring early in the ranking are assigned larger rank values. Therefore, the most abnormal data point is assigned a score (rank) of \(n\) out of \(n\) data points.
- 2.
This is a common misunderstanding of the Bonferroni principle [343].
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Aggarwal, C. (2015). Outlier Analysis: Advanced Concepts. In: Data Mining. Springer, Cham. https://doi.org/10.1007/978-3-319-14142-8_9
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DOI: https://doi.org/10.1007/978-3-319-14142-8_9
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