Outlier Detection with Two-Stage Area-Descent Method for Linear Regression

— Outlier detection is an important task in many applications; it can lead to the discovery of unexpected, useful or interesting objects in data analysis. Many outlier detection methods are available. However, they are limited by assumptions in distribution or rely on many patterns to detect one outlier. Often, a distribution is not known, or experimental results may not provide enough information about a set of data to be able to determine a certain distribution. Previous work in outlier detection based on area-descent focused on detecting outliers which are solely isolated; it can not detect the outliers clustered together. In this paper, we propose a new approach for outlier detection based on two-stage area-descent of convex-hull polygon. It not only detects outliers clustered together but also shows their location related to the data set. Instead of removing the outlier, this relative location provides a suitable direction for moving the outlier to reduce its effects to linear regression. In addition, this method does not depend on the distribution of data set. Key-Words: — Outlier detection, convex-hull, polygon, area-descent, linear regression.