Enhancing Effectiveness of Outlier Detections for Low Density Patterns

By Jian Tang, Zhixiang Chen, Ada Wai-Chee Fu, David Wai-Lok Cheung. Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2002

662 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/22e971f51c89f83d228b54735b0a7e488c7c1880

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Citations in the corpus

Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection (2015, methodology): “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].”
Anomaly Detection in Time Series: A Comprehensive Evaluation (2022, methodology): “distance community Python COF [130] Outlier Det.”
Ranking Outliers Using Symmetric Neighborhood Relationship (2006, methodology): “In [5], [4], three enhancement schemes over LOF are introduced, namely LOF’ and LOF” and GridLOF, and [22] introduces a connectivity-based outlier factor (COF) scheme that improves the effectiveness of an existing local outlier factor LOF scheme when a pattern itself has similar neighborhood density”
Interpreting and Unifying Outlier Scores (2011, methodology): “Several extensions of the basic LOF model and algorithms for efficient (topn ) computation have been proposed [22] [23] [49] [50] that basically use different notions of neighborhoods.”
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy (2021, methodology): “, 2000)) and connectivity outlier factor (COF, (Tang et al., 2002)) respectively calculate local density and local connectivity for outlier determination.”
Incremental Local Outlier Detection for Data Streams (2007, background): “In addition, an extension of the proposed methodology to create incremental versions of other emerging outlier detection algorithms, (e.g., Connectivity Outlier Factor (COF) [34], LOCI [35]), is also worth considering.”
ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions (2022, background): “A few improvements were later introduced, including the Connective-based Out-lier Factor (COF) by Tang et al. [35], and LOcal Correlation Integral (LOCI) by Papadimitriou et al. [36].”
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, background): “Connectivity-based outlier factors [155], influenced outlierness [156], and local”
Isolation-Based Anomaly Detection (2012, methodology): “Well-known methods in density-based approach are Local Outlier Factor (LOF) [Breunig et al. 2000], Connectivity-based Outlier Factor (COF) [Tang et al. 2002], LOcal Correlation Integral (LOCI) [Papadimitriou et al. 2003] and Resolution-based Outlier Factor (ROF) [Fan et al. 2006].”
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”
Outlier Analysis (2013, background): “The concept of connectivity-based outlier factor (COF) was proposed in [442], which is also able to find outliers in low density or arbitrarily shaped regions effectively.”