Discovering cluster-based local outliers

By Rfdo 2XWOLHUV. Pattern Recognition Letters, 2003

903 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability, Signal Processing

View Paper: https://www.semanticscholar.org/paper/d8021847ed0e3f26b53416c2a254a85451ee5f1e

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Citations in the corpus

Outlier Analysis (2013, methodology): “As shown in [209] direct adaptations of various methods such as replicator neural networks [464], and clustering methods [210] do not work effectively.”
Isolation-Based Anomaly Detection (2012, methodology): “In clustering-based methods [He et al. 2003], data are .rst clustered by a clustering algorithm; then, any small clusters or points that are distant from large cluster centroids are deemed anomalies.”
Knowledge graph refinement: A survey of approaches and evaluation methods (2016, methodology): “In that feature space, standard outlier detection techniques such as Local Outlier Factor [11] or cluster-based outlier detection [34] can be used to assign outlier scores.”
Isolation Forest (2008, methodology): “Apart from the key difference of isolation versus pro-ﬁling, iForest is distinguished from existing model-based [11, 1, 5], distance-based [6] and density-based methods [4] in the follow ways: • The isolation characteristic of iTrees enables them to build partial models and exploit sub-sampling to…”
Machine learning and computer vision approaches for phenotypic profiling (2017, methodology): “Many different algorithms exist for performing outlier detection based on statistics (Barnett and Lewis, 1994), distance (Knorr and Ng, 1998; Ramaswamy et al., 2000), density (Breunig et al., 2000), clustering (Jain et al., 1999; He et al., 2003), and deviation (Arning et al., 1996).”
Contextual Outlier Interpretation (2017, methodology): “ical model) is applied to the data, and outliers are those who do not t the model well [19, 42, 48].”
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, background): “Local density cluster based outlier factor [158], clustering basedmultivariate Gaussian outlier score [159], [160] and histogram based outlier score [161] are the famous cluster based outlier detection models in literature.”
A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data (2018, methodology): “Similarly, the clustering models (He, Xu, and Deng 2003; Campello et al. 2015) cluster different data samples and ﬁnd anomalies via a predeﬁned outlierness score.”
Enhancing one-class support vector machines for unsupervised anomaly detection (2013, methodology): “Table 3: CPU execution time of SVM based algorithms Algorithm ionosphere [ms] shuttle [s] breast-cancer [ms] satellite [s] One-class Robust one-class Eta one-class 21.55 ± 0.26 33.82 ± 0.26 27.48 ± 0.25 747.15 ± 10.94 218.93 ± 3.17 4.07 ± 0.14 48.72 ± 1.01 57.27 ± 2.29 82.46 ± 0.42 14.02 ± 2.00 8”
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.”
A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data (2016, methodology): “As already pointed out, the local density estimation of CBLOF using only the amount of cluster members is controversial.”
Graph based anomaly detection and description: a survey (2014, background): “…Ng, 1998, Orair et al., 2010, Wang et al., 2011b], depth-based [Ruts and Rousseeuw, 1996], distribution-based [Saltenis, 2004], clustering-based [He et al., 2003, Lieto et al., 2008, Miller and Browning, 2003, Wang et al., 2012c], classification-based [Abe et al., 2006, Hempstalk et al., 2008,…”
DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series (2019, methodology): “As name suggests, Cluster-Based Local Outlier Factor (CBLOF) [15] is a clustering based anomaly detection algorithm, in which data points are clustered using k-means (or any other) clustering algorithm.”
PyOD: A Python Toolbox for Scalable Outlier Detection (2019, background): “, 2000) Proximity No Yes AvgkNN (Angiulli and Pizzuti, 2002) Proximity No Yes CBLOF (He et al., 2003) Proximity Yes No OCSVM (Ma and Perkins, 2003) Linear Model No No LOCI (Papadimitriou et al.”
Anomaly Detection in Time Series: A Comprehensive Evaluation (2022, methodology): “Representatives of this category in our evaluation are: CBLOF [59], COF [130], DBStream [55], HOT SAX [70], Hybrid KNN [124], k-Means [151], KNN [110], LOF [22], NormA-SJ [15], PhaseSpaceSVM (PS-SVM) [85], SAND [17], SSA [155], STAMP [156], STOMP [164], Sub-LOF [22], VALMOD [82], and Left STAMPi [15”