Cited by 864Anomaly Detection Techniques and ApplicationsAdvanced Statistical Methods and ModelsAdvanced Clustering Algorithms Research

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

Zimek, Arthur, Schubert, Erich, Kriegel, Hans‐Peter·2012·Statistical Analysis and Data Mining The ASA Data Science Journal

Abstract

Abstract 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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Crossref · 10.1002/sam.11161

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Authors
Zimek, A., Schubert, E., & Kriegel, H.
Affiliations
  • University of Alberta (CA)
  • Ludwig-Maximilians-Universität München (DE)
Corresponding author
Zimek, Arthur
Venue
Statistical Analysis and Data Mining The ASA Data Science Journal
Publisher
Wiley
Published
2012-08-27
Volume
5
Issue
5
Pages
363-387
ISSN
  • 1932-1864 (ISSN-L)
  • 1932-1872
OpenAlex
W2015887370
Cited by
864
References
166
Citations by year
2026: 42 · 2025: 62 · 2024: 68 · 2023: 71 · 2022: 72 · 2021: 100 · 2020: 79 · 2019: 94 · 2018: 79 · 2017: 52 · 2016: 68 · 2015: 33 · 2014: 26 · 2013: 13 · 2012: 4
FWCI
23.75
Type
journal article
Language
en
Open access
closed
Locations
Statistical Analysis and Data Mining The ASA Data Science Journal (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
Keywords
unsupervised anomaly detection, high-dimensional data, curse of dimensionality, irrelevant attributes, Euclidean space, outlier detection algorithms

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