Outlier Detection: A Clustering-Based Approach
Vijay Kumar1, Sunil Kumar2, Ajay Kumar Singh3
1Asst. Prof. Vijay Kumar, Department of CSE MIET, Meerut (U.P.), India.
Sunil Kumar, Associate, Professor, Department of CSE, JPIET, Meerut (U.P.), India.
3Prof. (Dr.) Ajay Kumar Singh, Department of CSE MIET, Meerut (U.P.), India.
Manuscript received on June 05, 2013. | Revised Manuscript received on June 11, 2013. | Manuscript published on June 15, 2013. | PP: 16-19 | Volume-1 Issue-7, June 2013. | Retrieval Number: G0334061713/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

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..
Keywords: PAM, Clustering, Clustering-based outliers, Outlier detection.