A Distributed Algorithm for the Cluster-Based Outlier Detection Using Unsupervised Extreme Learning Machines. (9th April 2017)

• Record Type: Journal Article
• Authors: Wang, Xite; Bai, Mei; Shen, Derong; Nie, Tiezheng; Kou, Yue; Yu, Ge
• Other Names: Borboni Alberto Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2017(2017)
• Publication Date: 2017-04-09
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2017/2649535
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Outlier detection is an important data mining task, whose target is to find the abnormal or atypical objects from a given dataset. The techniques for detecting outliers have a lot of applications, such as credit card fraud detection and environment monitoring. Our previous work proposed the Cluster-Based (CB) outlier and gave a centralized method using unsupervised extreme learning machines to compute CB outliers. In this paper, we propose a new distributed algorithm for the CB outlier detection (DACB). On the master node, we collect a small number of points from the slave nodes to obtain a threshold. On each slave node, we design a new filtering method that can use the threshold to efficiently speed up the computation. Furthermore, we also propose a ranking method to optimize the order of cluster scanning. At last, the effectiveness and efficiency of the proposed approaches are verified through a plenty of simulation experiments.