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

Xite Wang, Mei Bai, Derong Shen, Tiezheng Nie, Yue Kou and Ge Yu

Mathematical Problems in Engineering, 2017, vol. 2017, 1-12

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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:2649535

DOI: 10.1155/2017/2649535

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