Outlier Detection in Big Data

J. Wang; Victoria J. Hodge

doi:10.4018/978-1-4666-5202-6

Outlier Detection in Big Data

J. Wang (Editor), Victoria J. Hodge

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

Abstract

Outlier detection (or anomaly detection) is a fundamental task in data mining. Outliers are data that deviate from the norm and outlier detection is often compared to “finding a needle in a haystack”. However, the outliers may generate high value if they are found, value in terms of cost savings, improved efficiency, compute time savings, fraud reduction and failure prevention. Detection can identify faults before they escalate with potentially catastrophic consequences. Big Data refers to large, dynamic collections of data. These vast and complex data appear problematic for traditional outlier detection methods to process but, Big Data provides considerable opportunity to uncover new outliers and data relationships. This chapter highlights some of the research issues for outlier detection in Big Data and covers the solutions used and research directions taken along with an analysis of some current outlier detection approaches for Big Data applications.

Original language	English
Title of host publication	Encyclopedia of Business Analytics and Optimization
Editors	J. Wang
Publisher	Hershey, PA: IGI Global
Pages	1762-1771
Number of pages	10
DOIs	https://doi.org/10.4018/978-1-4666-5202-6
Publication status	Published - 1 Apr 2014

Publication series

Name	Encyclopedia of Business Analytics and Optimization

Bibliographical note

I have been given permission to publish this version of the chapter on Uni of York research database. I have a signed authorisation form from IGI in PDF format giving authorisation.

Access to Document

10.4018/978-1-4666-5202-6

hodge article_wang ency 2014Licence: Unspecified

http://www.igi-global.com/book/encyclopedia-business-analytics-optimization/90651

Cite this

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