A distributed algorithm for the cluster-based outlier detection using unsupervised extreme learning machines
type: Item
instance of: scholarly article
title: A distributed algorithm for the cluster-based outlier detection using unsupervised extreme learning machines
zbMATH Open document ID: 1426.62190
DOI: 10.1155/2017/2649535
author: Xite Wang
author: Mei Bai
author: Derong Shen
author: Tiezheng Nie
author: Yue Kou
author: Ge Yu
published in: Mathematical Problems in Engineering
publication date: 2018-11-05T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 62F03
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 68W15
zbMATH DE Number: 6971891
Wikidata QID: Q59147419
describes a project that uses: LOF
describes a project that uses: Orca
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/2649535
OpenAlex ID: W2605048794
cites work: Q3880088
cites work: Q4302491
cites work: Support-vector networks
cites work: Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
cites work: Learning Deep Architectures for AI
cites work: Discovering cluster-based local outliers
cites work: Q3147443
description: scientific article; zbMATH DE number 6971891