Strategies for outlier analysis

The handling of anomalous or outlying observations in a data set is one of the most important tasks in data pre-processing. It is important for three reasons. First, outlying observations can have a considerable influence on the results of an analysis. Second, although outliers are often measurement or recording errors, some of them can represent phenomena of interest, something significant from the viewpoint of the application domain. Third, for many applications, exceptions identified can often lead to the discovery of unexpected knowledge