Outliers are extreme values, typically several standard deviations from the mean. To minimize the effect of outliers, you can Winsorize or trim the data.
Winsorizing involves setting the tail values of an attribute to some specified value. For example, for a 90% Winsorization, the bottom 5% of values are set equal to the minimum value in the 5th percentile, while the upper 5% of values are set equal to the maximum value in the 95th percentile.
Trimming sets the tail values to NULL. The algorithm treats them as missing values.
Outliers affect the different algorithms in different ways. In general, outliers cause distortion with equi-width binning and min-max normalization.
Table 4-6 Outlier Treatment Methods in DBMS_DATA_MINING_TRANSFORM
| Transformation | Description |
|---|---|
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This technique trims the outliers in numeric columns by sorting the non-null values, computing the tail values based on some fraction, and replacing the tail values with nulls. |
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This technique trims the outliers in numeric columns by sorting the non-null values, computing the tail values based on some fraction, and replacing the tail values with some specified value. |