Outlier Detection: A Clustering-Based Approach
International Journal of Innovative Science and Modern Engineering (IJISME), 1 (7):
16-19 (June 2013)Abstract
Outlier detection is a fundamental issue in data mining; specifically it has been used to detect and remove anomalous objects from data. It is an extremely important task in a wide variety of application domains. In this paper, a proposed method based on clustering approaches for outlier detection is presented. We first perform the Partitioning Around Medoids (PAM) clustering algorithm. Small clusters are then determined and considered as outlier clusters. The rest of outliers (if any) are then detected in the remaining clusters based on calculating the absolute distances between the medoid of the current cluster and each one of the points in the same cluster. Experimental results show that our method works well.