Detecting Outliers in Datasets


Datasets may contain outliers or bad observations that do not belong to the distribution to be analyzed. The cause may be an unreliable process of data collection, as in the case of using micro-array technologies for measurement of gene expression levels, or intentional actions, such as network intrusion. Outliers can lead to biased estimates and wrong conclusions about the object.

To process datasets with outliers, you can choose between the BACON outlier detection algorithm and robust methods considered in the following sections.

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Notice revision #20110804

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