Research Article
Advancements of Outlier Detection: A Survey
@ARTICLE{10.4108/trans.sis.2013.01-03.e2, author={Ji Zhang}, title={Advancements of Outlier Detection: A Survey}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={1}, number={1}, publisher={ICST}, journal_a={SIS}, year={2013}, month={2}, keywords={Data Mining, Outlier Detection, High-dimensional Datasets}, doi={10.4108/trans.sis.2013.01-03.e2} }- Ji Zhang
Year: 2013
Advancements of Outlier Detection: A Survey
SIS
ICST
DOI: 10.4108/trans.sis.2013.01-03.e2
Abstract
Outlier detection is an important research problem in data mining that aims to discover useful abnormal and irregular patterns hidden in large datasets. In this paper, we present a survey of outlier detection techniques to reflect the recent advancements in this field. The survey will not only cover the traditional outlier detection methods for static and low dimensional datasets but also review the more recent developments that deal with more complex outlier detection problems for dynamic/streaming and high-dimensional datasets.
Copyright © 2013 Zhang, licensed to ICST. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
