Outlier detection and evaluation by network flow

Detecting outliers is an important topic in data mining. Sometimes the outliers are more interesting than the rest of the data. Outlier identification has lots of applications, such as intrusion detection, and unusual usage of credit cards or telecommunication services. In this paper, we propose a novel method for outlier identification which is based on network flow. We use the well known Maximum Flow Minimum Cut theorem from graph theory to find the outliers and strong outlier groups, and evaluate the outliers by the volume of the flow. This outlier detection occurs in a novel setting: to repair poor quality clusters generated by a clustering algorithm.