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Adaptive outlierness for subspace outlier ranking
Proceedings of the 19th ACM international conference on Information and knowledge management, 2010Outlier mining is an important data analysis task to distinguish exceptional outliers from regular objects. However, in recent applications traditional outlier mining approaches miss outliers as they are hidden in subspace projections. In this work, we propose a novel outlier ranking based on the degree of deviation in subspaces.
Emmanuel Müller +2 more
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On Evaluation of Outlier Rankings and Outlier Scores
Proceedings of the 2012 SIAM International Conference on Data Mining, 2012Outlier detection research is currently focusing on the development of new methods and on improving the computation time for these methods. Evaluation however is rather heuristic, often considering just precision in the top k results or using the area under the ROC curve.
Erich Schubert +3 more
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WIREs Data Mining and Knowledge Discovery, 2011
AbstractOutlier detection is an area of research with a long history which has applications in many fields. This article provides a nontechnical and concise overview of the commonly used approaches for detecting outliers, including classical methods, new challenges posed by real‐world massive data, and some of the key advances made in recent years ...
Xiaogang Su, Chih-Ling Tsai
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AbstractOutlier detection is an area of research with a long history which has applications in many fields. This article provides a nontechnical and concise overview of the commonly used approaches for detecting outliers, including classical methods, new challenges posed by real‐world massive data, and some of the key advances made in recent years ...
Xiaogang Su, Chih-Ling Tsai
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Enhancing Outlier Detection by an Outlier Indicator
2018Outlier detection is an important task in data mining and has high practical value in numerous applications such as astronomical observation, text detection, fraud detection and so on. At present, a large number of popular outlier detection algorithms are available, including distribution-based, distance-based, density-based, and clustering-based ...
Xiaqiong Li, Xiaochun Wang, Xia Li Wang
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Hastings Center Report, 2013
AbstractAmy's excellent home care was the only reason she was doing as well as she was. There was no one better at taking care of Amy than her husband. When she had a respiratory infection, Steve managed the necessary increased suctioning, nebulizer treatments, and ventilator, in addition to all the other intimate personal assistance she needed on a ...
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AbstractAmy's excellent home care was the only reason she was doing as well as she was. There was no one better at taking care of Amy than her husband. When she had a respiratory infection, Steve managed the necessary increased suctioning, nebulizer treatments, and ventilator, in addition to all the other intimate personal assistance she needed on a ...
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When is an outlier not an outlier?
NIR news, 2019The tendency of the users of NIRS is to think of the composition particularly in terms of the reference data, for the prediction of which the technique is to be used. The concept and principle of NIRS analysis differ from classical chemical analysis in that NIRS is based exclusively on the spectra.
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2005
We develop exact and approximate algorithms for computing optimal separators and measuring the extent to which two point sets in d-dimensional space are separated, with respect to different classes of separators and various extent measures. This class of geometric problems generalizes two widely studied problem families, namely separability and the ...
Sariel Har-Peled, Vladlen Koltun
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We develop exact and approximate algorithms for computing optimal separators and measuring the extent to which two point sets in d-dimensional space are separated, with respect to different classes of separators and various extent measures. This class of geometric problems generalizes two widely studied problem families, namely separability and the ...
Sariel Har-Peled, Vladlen Koltun
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Proceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing, 2019
Outlier detection is a fundamental issue in data mining and machine learning. Most methods calculate outlier score for each object and then threshold the scores to detect outliers. Most widely used thresholding techniques are based on statistics like standard deviation around mean, median absolute deviation and interquartile range. Unfortunately, these
Jiawei Yang 0001 +2 more
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Outlier detection is a fundamental issue in data mining and machine learning. Most methods calculate outlier score for each object and then threshold the scores to detect outliers. Most widely used thresholding techniques are based on statistics like standard deviation around mean, median absolute deviation and interquartile range. Unfortunately, these
Jiawei Yang 0001 +2 more
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Proceedings of the nineteenth conference on Computational geometry - SCG '03, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sariel Har-Peled, Yusu Wang 0001
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sariel Har-Peled, Yusu Wang 0001
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