A comprehensive survey of numeric and symbolic outlier mining techniques

Agyemang, Malik; Barker, Ken; Alhajj, R.

doi:10.3233/ida-2006-10604

Cited by 119 publications

(43 citation statements)

References 61 publications

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“…Though in recent years, several surveys on outlier detection have been published (see refs. [1–8], to name a few), the difficulties in high‐dimensional data and specialized approaches in this area have not been sketched in any of those (though, notably, a recent textbook edition sketches three example approaches [9]). In fact, most approaches to this problem have been proposed just recently in the past two or three years.…”

Section: Introductionmentioning

confidence: 99%

A survey on unsupervised outlier detection in high‐dimensional numerical data

Zimek

Schubert

Kriegel

2012

Statistical Analysis

685

413

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High‐dimensional data in Euclidean space pose special challenges to data mining algorithms. These challenges are often indiscriminately subsumed under the term ‘curse of dimensionality’, more concrete aspects being the so‐called ‘distance concentration effect’, the presence of irrelevant attributes concealing relevant information, or simply efficiency issues. In about just the last few years, the task of unsupervised outlier detection has found new specialized solutions for tackling high‐dimensional data in Euclidean space. These approaches fall under mainly two categories, namely considering or not considering subspaces (subsets of attributes) for the definition of outliers. The former are specifically addressing the presence of irrelevant attributes, the latter do consider the presence of irrelevant attributes implicitly at best but are more concerned with general issues of efficiency and effectiveness. Nevertheless, both types of specialized outlier detection algorithms tackle challenges specific to high‐dimensional data. In this survey article, we discuss some important aspects of the ‘curse of dimensionality’ in detail and survey specialized algorithms for outlier detection from both categories. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2012

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Section: Introductionmentioning

confidence: 99%

A survey on unsupervised outlier detection in high‐dimensional numerical data

Zimek

Schubert

Kriegel

2012

Statistical Analysis

685

413

View full text Add to dashboard Cite

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“…For the interested reader we refer to several examples of published surveys which are concerned with these topics (e.g. [4], [5], [6]). …”

Section: Related Workmentioning

confidence: 99%

A New Approach for Outlier Detection in Near Real Time

Eiweck

Pattinson

Behringer

et al. 2010

2010 Fourth UKSim European Symposium on Computer Modeling and Simulation

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Outlier detection methods have been suggested for a broad range of applications. They are obligatory in different monitoring tasks (e.g. mobile phone monitoring, credit card usage monitoring). Aim is the detection of sudden changes in the usage pattern which may indicate deviations from normal usage. Parametric (statistical) and non parametric methods combined with univariate and multivariate methods form most of the body of research in anomaly detection. In this paper a novel approach for outlier detection is proposed. For the validation of the new approach we have applied the method on voice traffic time-series obtained from a real-life mobile network.Main focus is the incorporation of standard statistical methods, the ability to implement specific heuristics derived from domain expert knowledge and near real-time performance. The reliability achieved with this novel approach for outlier detection is sufficient to be used in operational processes. Enabling the detection of outliers in near real time supports a broad range of monitoring tasks to be performed by the operational staff.

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“…al. [7], and Hodge and Austin [8], have been published. In this subsection, we review FPGA-based anomaly detection systems.…”

Section: B Anomaly Detectionmentioning

confidence: 99%

An FPGA-based spectral anomaly detection system

Moss

Zhang

Fraser

et al. 2014

2014 International Conference on Field-Programmable Technology (FPT)

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Anomaly detection based on spectral features is applicable to a diverse range of problems including prognostic and health management, vibration analysis, astronomy, biomedical engineering and computational finance. The input data could be regularly sampled, as in the case of a standard analogue to digital converter sampling a bandlimited signal at above the Nyquist rate, or irregularly sampled, as in the case of stock quotes or astronomical data. In this paper, we present new online algorithms for the computation of power spectra for regularly or irregularly sampled data, and performing anomaly detection on time series data. Both algorithms allow hardware implementations with O(1) time complexity, this being the minimum for any system that considers all the samples. We combine the two algorithms to form a power Spectrum-based Anomaly Detector (SAD). We also describe an implementation of SAD which has minimal hardware requirements, and achieves one to two orders of magnitude improvement in speed, latency, power and energy over a traditional processor-based design.978-1-4799-6245-7/14/$31.00 ©2014 IEEE

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A comprehensive survey of numeric and symbolic outlier mining techniques

Cited by 119 publications

References 61 publications

A survey on unsupervised outlier detection in high‐dimensional numerical data

A survey on unsupervised outlier detection in high‐dimensional numerical data

A New Approach for Outlier Detection in Near Real Time

An FPGA-based spectral anomaly detection system

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