Outlier Detection: A Clustering-Based Approach
Vijay Kumar, Sunil Kumar, Ajay Singh · 2013
Outlier detection is a fundamental issue i n data mining; specifically it has been used to detect and remove anomalous objects from data. It is an extremely imp ortant 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 out liers (if any) are then detected in the remaining clusters based on ca lculating the absolute distances between the medoid of the current cluster and each one of the points in the same cluster. Experime ntal results show that our method works well.