By the same authors

Outlier Detection in Big Data

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

Standard

Outlier Detection in Big Data. / Wang, J. (Editor); Hodge, Victoria J.

Encyclopedia of Business Analytics and Optimization. ed. / J. Wang. Hershey, PA: IGI Global, 2014. p. 1762-1771 (Encyclopedia of Business Analytics and Optimization).

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

Harvard

Wang, J (ed.) & Hodge, VJ 2014, Outlier Detection in Big Data. in J Wang (ed.), Encyclopedia of Business Analytics and Optimization. Encyclopedia of Business Analytics and Optimization, Hershey, PA: IGI Global, pp. 1762-1771. https://doi.org/10.4018/978-1-4666-5202-6

APA

Wang, J. (Ed.), & Hodge, V. J. (2014). Outlier Detection in Big Data. In J. Wang (Ed.), Encyclopedia of Business Analytics and Optimization (pp. 1762-1771). (Encyclopedia of Business Analytics and Optimization). Hershey, PA: IGI Global. https://doi.org/10.4018/978-1-4666-5202-6

Vancouver

Wang J, (ed.), Hodge VJ. Outlier Detection in Big Data. In Wang J, editor, Encyclopedia of Business Analytics and Optimization. Hershey, PA: IGI Global. 2014. p. 1762-1771. (Encyclopedia of Business Analytics and Optimization). https://doi.org/10.4018/978-1-4666-5202-6

Author

Wang, J. (Editor) ; Hodge, Victoria J. / Outlier Detection in Big Data. Encyclopedia of Business Analytics and Optimization. editor / J. Wang. Hershey, PA: IGI Global, 2014. pp. 1762-1771 (Encyclopedia of Business Analytics and Optimization).

Bibtex - Download

@inbook{2fecab34947f47649f33358204da8e56,
title = "Outlier Detection in Big Data",
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.",
author = "J. Wang and Hodge, {Victoria J.}",
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.",
year = "2014",
month = "4",
day = "1",
doi = "10.4018/978-1-4666-5202-6",
language = "English",
series = "Encyclopedia of Business Analytics and Optimization",
publisher = "Hershey, PA: IGI Global",
pages = "1762--1771",
editor = "J. Wang",
booktitle = "Encyclopedia of Business Analytics and Optimization",

}

RIS (suitable for import to EndNote) - Download

TY - CHAP

T1 - Outlier Detection in Big Data

AU - Hodge, Victoria J.

A2 - Wang, J.

A2 - Wang, J.

N1 - 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.

PY - 2014/4/1

Y1 - 2014/4/1

N2 - 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.

AB - 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.

U2 - 10.4018/978-1-4666-5202-6

DO - 10.4018/978-1-4666-5202-6

M3 - Chapter (peer-reviewed)

T3 - Encyclopedia of Business Analytics and Optimization

SP - 1762

EP - 1771

BT - Encyclopedia of Business Analytics and Optimization

PB - Hershey, PA: IGI Global

ER -