Date Added: Feb 2012
Data objects which do not comply with the general behavior or model of the data are called Outliers. Outlier Detection in databases has numerous applications such as fraud detection, customized marketing, and the search for terrorism. However, the use of Outlier Detection for various purposes is not an easy task. In this paper, the authors propose a technique for detecting outliers in an easier manner using clustering. They analyze their technique to clearly distinguish the normal data from outliers.