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

Abstract EN

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.