Outlier Analysis: Outlier detection refers to the problem of finding patterns in data that are very different from the rest of the data based on appropriate metrics.
Such a pattern often contains useful information regarding abnormal behavior of the system described by the data.
Distance based algorithms calculate the distances among objects in the data with geometric interpretation.
Density-based algorithms estimate the density distribution of the input space and then identify outliers as those lying in low density.
Rough sets based algorithms introduce rough sets or fuzzy rough sets to identify outliers.
Based on the article that mention up answer the question:
What is the main goal of Outlier analysis?