Visual Evaluation of Outlier Detection Models

Achtert, Elke; Kriegel, Hans‐Peter; Reichert, Lisa; Schubert, Erich; Wojdanowski, Remigius; Zimek, Arthur

doi:10.1007/978-3-642-12098-5_34

Cited by 27 publications

(18 citation statements)

References 11 publications

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“…The generalization of 6-distance neighborhoods method introduced 3 and 19 false alarms of approximatively equal magnitude, and the Extended-SAX-based outlier detection method introduced 3 and 7 false alarms of approximatively equal magnitude. Unlike the other two compared approaches, the proposed outlier detection method shows a strong peak for the range of the outlier subsequence, as it successfully detected the outlier 11 . Although 3 , 7 , and 19 are not real outliers, the proposed outlier detection method also shows 3 and 19 at a relatively high outlier "level," but no more than that of the real outlier 11 .…”

Section: Experiments 1 (Keogh Data) Keogh Data [21]mentioning

confidence: 98%

“…Unlike the other two compared approaches, the proposed outlier detection method shows a strong peak for the range of the outlier subsequence, as it successfully detected the outlier 11 . Although 3 , 7 , and 19 are not real outliers, the proposed outlier detection method also shows 3 and 19 at a relatively high outlier "level," but no more than that of the real outlier 11 . This situation indicates that the proposed outlier detection algorithm might have a practical application value.…”

Section: Experiments 1 (Keogh Data) Keogh Data [21]mentioning

confidence: 98%

“…The associated outlier detection methods that are developed from the four types of distance all inherit their own advantages or disadvantages without exception. ED is well known for its simple computation and sound universality, but it can only carry out the time series of equal length and cannot recognize the variation trend of time series [13,14]. DTW can well overcome the first disadvantage of ED and can support the time warping of time series.…”

Section: Related Workmentioning

confidence: 99%

“…, 20 }, which is implemented for the experiment. In the range between the 400th and 432nd data points, the corresponding 11th (e.g., 11 ) subsequence is the real outlier. In the feature spacebased outlier detection method, the number of subsequences is 20; in -distance is 6; the parameter in Extended-SAX-based outlier detection method is 4; and Max in the proposed outlier detection method is 2.…”

Section: Experiments 1 (Keogh Data) Keogh Data [21]mentioning

confidence: 99%

“…Some of the outlier detection approaches are based on notably different assumptions, intuitions, and models and also differ substantially in the scaling, range, and even meaning of values [11]. Furthermore, some other methods are developed on the basis of the technologies themselves such as the cluster-based detection method [4], the immunologybased detection method [12], and the SVM-based detection method [8].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Locality-Based Visual Outlier Detection Algorithm for Time Series

Li¹,

Li²,

Yu³

et al. 2017

Security and Communication Networks

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Physiological theories indicate that the deepest impression for time series data with respect to the human visual system is its extreme value. Based on this principle, by researching the strategies of extreme-point-based hierarchy segmentation, the hierarchysegmentation-based data extraction method for time series, and the ideas of locality outlier, a novel outlier detection model and method for time series are proposed. The presented algorithm intuitively labels an outlier factor to each subsequence in time series such that the visual outlier detection gets relatively direct. The experimental results demonstrate the average advantage of the developed method over the compared methods and the efficient data reduction capability for time series, which indicates the promising performance of the proposed method and its practical application value.

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Section: Experiments 1 (Keogh Data) Keogh Data [21]mentioning

confidence: 98%

Section: Experiments 1 (Keogh Data) Keogh Data [21]mentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

Section: Experiments 1 (Keogh Data) Keogh Data [21]mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Locality-Based Visual Outlier Detection Algorithm for Time Series

Li¹,

Li²,

Yu³

et al. 2017

Security and Communication Networks

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

Zimek

Schubert

Kriegel

2012

Statistical Analysis

Self Cite

686

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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Density‐based clustering

Kriegel

Kröger

Sander

et al. 2011

WIREs Data Min & Knowl

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660

341

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Clustering refers to the task of identifying groups or clusters in a data set. In density-based clustering, a cluster is a set of data objects spread in the data space over a contiguous region of high density of objects. Density-based clusters are separated from each other by contiguous regions of low density of objects. Data objects located in low-density regions are typically considered noise or outliers. In this review article we discuss the statistical notion of density-based clusters, classic algorithms for deriving a flat partitioning of density-based clusters, methods for hierarchical density-based clustering, and methods for semi-supervised clustering. We conclude with some open challenges related to density-based clustering.

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Visual Evaluation of Outlier Detection Models

Cited by 27 publications

References 11 publications

Locality-Based Visual Outlier Detection Algorithm for Time Series

Locality-Based Visual Outlier Detection Algorithm for Time Series

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

Density‐based clustering

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