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