期刊名称：International Journal on Computer Science and Engineering
印刷版ISSN：2229-5631
电子版ISSN：0975-3397
出版年度：2011
卷号：3
期号：1
页码：333-339
出版社：Engg Journals Publications
摘要：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. In these algorithms, outliers are only by-products of clustering algorithms and they cannot rank the priority of outliers. In this paper, three partition-based algorithms, PAM, CLARA and CLARANs are combined with k-medoid distance based outlier detection to improve the outlier detection and removal process. The experimental results prove that CLARANS clustering algorithm when combined with medoid distance based outlier detection improves the accuracy of detection and increases the time efficiency.
