Distance-based outliers: algorithms and applications

By Edwin M. Knorr, Raymond T. Ng, Vladimir Tucakov. The VLDB journal, 2000

1,321 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/154df96b95e8b9635771442244fe48b125933bb1

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Citations in the corpus

Mining distance-based outliers in near linear time with randomization and a simple pruning rule (2003, methodology): “Other researchers, beginning with the work by Knorr and Ng [16], have taken a non-parametric approach and proposed using an example’s distance to its nearest neighbors as a measure of unusualness [2, 10, 17, 19].”
A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation (2021, methodology): “For example, Knorr and Lu [61, 62] use spatial distance measures to compute anomaly scores for spatial objects; while Shekhar and Kou [63, 64] use graphical distance measures for spatial objects.”
Outlier Analysis (2013, methodology): “The earliest methods for trajectory shape outlier detection were proposed in [263].”
A Survey of Privacy-Preserving Methods Across Vertically Partitioned Data (2008, neutral): “Other distance based outlier techniques also exist[18, 22].”
Identifying and Handling Mislabelled Instances (2004, background): “, in the cases of fraud detection or records (Knorr et al., 2000)) and those which look into limiting their noisy effects (John, 1995; Brodley and Friedl, 1996, 1999).”
Trajectory Outlier Detection: A Partition-and-Detect Framework (2008, methodology): “These algorithms are classified mainly into four classes: distribution-based [2], distancebased [3], [4], [5], [6], density-based [7], [8], and deviationbased [9].”
Deep Learning for Anomaly Detection (2020, background): “implement. There have been a number of ACM Comput. Surv., Vol. xx, No. x, Article . Publication date: July 202x. 16 Pang, et al. effective distance-based anomaly measures introduced,e.g., DB outliers [73, 74], k-nearest neighbor distance [126, 127], average k-nearest neighbor distance [9], relative ”
iBAT: detecting anomalous taxi trajectories from GPS traces (2011, methodology): “The proposed methods range from supervised approaches [1] to distance-based [12, 3], density-based [5], and model-based methods [11], each having a specific formulation of the problem with different anomaly measures and notions.”
Generative Probabilistic Novelty Detection with Adversarial Autoencoders (2018, background): “istribution of inliers by learning the parameters deﬁning the probability, and outliers are identiﬁed as those having low probability under the learned model. Distance based outlier detection methods [20, 17, 21] identify outliers by their distance to neighboring examples. They assume that inliers a”
Outlier mining in large high-dimensional data sets (2005, neutral): “real-lifeapplication domains, as showed in [ 16 ].”
Spatio-Temporal Data Mining (2017, methodology): “, using spatial distance measures (Knorr and Ng 1997; Knorr et al. 2000; Lu et al. 2003a), graphicaldistance measures (Kou et al.”
LOCI: fast outlier detection using the local correlation integral (2003, background): “The salient approaches to outlier detection can be classified as either distribution-based [5], depthbased [16], clustering [14], distance-based [17, 20, 18, 19], or density-based [8] (see Section 2).”
Language models for detection of unknown attacks in network traffic (2007, methodology): “Our method Zeta [44] is an anomaly score based on the concept of k-nearest neighbors; it extends the outlier detection methods proposed in [45; 46].”
Robust Random Cut Forest Based Anomaly Detection on Streams (2016, background): “For points in Rn, Knorr & Ng (1997; 1998; 1999); Knorr et al. (2000) estimate the density by looking at the number of points that are within a ball of radius d of a given data point.”
A comparative study of RNN for outlier detection in data mining (2002, methodology): “Of course, there are many data mining outlier detection methods not included here [9]and also many omitted statistical outlier methods [14, 11] Many of these methods are related to the three included methods and RNNs, often being adapted from clustering methods in one way or another and a full appra”
Interestingness measures for data mining: A survey (2006, background): “Peculiar patterns are generated from peculiar data (or outliers), which are relatively few in number and significantly different from the rest of the data [Knorr et al. 2000; Zhong et al. 2003].”
Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection (2015, background): “Some authors criticize the use of clustering algorithms as an approach to detect outliers [Knorr and Ng 1998; Ramaswamy et al. 2000; Knorr et al. 2000; Tang et al. 2002; Kriegel et al. 2010; Zimek et al. 2012].”
A survey on unsupervised outlier detection in high‐dimensional numerical data (2012, neutral): “…high-dimensional numeric data in Euclidean space), usually rely on the assessment of neighborhoods (e.g., counting objects in ε - neighborhoods [70,71], using k th-nearest-neighbor-distances or aggregates thereof as outlier score [27,72], or even comparing the object’s neighbors with the…”