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Outlier detection algorithm based on k-nearest neighbors-local outlier factor
Journal of Algorithms and Computational Technology, 2022 The main task of outlier detection is to detect data objects which have a different mechanism from the conventional data set. The existing outlier detection methods are mainly divided into two directions: local outliers and global outliers. Aiming at the
He Xu, Lin Zhang, Peng Li
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Progress in Outlier Detection Techniques: A Survey
IEEE Access, 2019 Detecting outliers is a significant problem that has been studied in various research and application areas. Researchers continue to design robust schemes to provide solutions to detect outliers efficiently. In this survey, we present a comprehensive and
Hongzhi Wang +2 more
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A survey on outlier explanations
VLDB Journal, 2022 While many techniques for outlier detection have been proposed in the literature, the interpretation of detected outliers is often left to users. As a result, it is difficult for users to promptly take appropriate actions concerning the detected outliers.
Egawati Panjei +2 more
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BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs
Neural Information Processing Systems, 2022Detecting which nodes in graphs are outliers is a relatively new machine learning task with numerous applications. Despite the proliferation of algorithms developed in recent years for this task, there has been no standard comprehensive setting for ...
Kay Liu +14 more
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A critical overview of outlier detection methods
Computer Science Review, 2020One of the opening steps towards obtaining a reasoned analysis is the detection of outlaying observations. Even if outliers are often considered as a miscalculation or noise, they may bring significant information.
Abir Smiti
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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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On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study
Data Mining and Knowledge Discovery, 2016Arthur Zimek +2 more
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QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs
Neural Information Processing SystemsWe introduce QuaRot, a new Quantization scheme based on Rotations, which is able to quantize LLMs end-to-end, including all weights, activations, and KV cache in 4 bits.
Saleh Ashkboos +8 more
semanticscholar +1 more source
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 ...
openaire +2 more sources