Performance Analysis of Clustering Methods for Outlier Detection

Outliers detection is a task that finds objects that are dissimilar or inconsistent with respect to the remaining data. It has many uses in applications like fraud detection, network intrusion detection and clinical diagnosis of diseases. Using clustering algorithms for outlier detection is a technique that is frequently used. The clustering algorithms consider outlier detection only to the point they do not interfere with the clustering process. This paper compares the performance of the four algorithms on outlier detection efficiency. The main objective is to detect outliers while simultaneously perform clustering operation.