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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 2649535, 12 pages
Research Article

A Distributed Algorithm for the Cluster-Based Outlier Detection Using Unsupervised Extreme Learning Machines

1College of Information Science & Technology, Dalian Maritime University, Dalian, Liaoning 116000, China
2College of Information Science & Engineering, Northeastern University, Shenyang, Liaoning 110819, China

Correspondence should be addressed to Xite Wang

Received 25 November 2016; Accepted 13 March 2017; Published 9 April 2017

Academic Editor: Alberto Borboni

Copyright © 2017 Xite Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.