Ranking Outliers Using Symmetric Neighborhood Relationship

By Wen Jin, Anthony K. H. Tung, Jiawei Han, Wei Wang. Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2006

467 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6a2b5f45130337fe44b86bd611371ab0bb40f4bb

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Citations in the corpus

A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data (2016, methodology): “For computing the INFLO score, both neighborhood sets are combined.”
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.”
DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series (2019, background): “This shortcoming is further resolved in Influenced Outlierness (INFLO) [14] algorithm.”
Outlier Analysis (2013, methodology): “Therefore, the INFLO technique of [242] takes the symmetric neighbor relationship into account while defining the local outliers.”
Progress in Outlier Detection Techniques: A Survey (2019, methodology): “Some of the density-based techniques [8], [75], [81], [82] have served as a fundamental baseline for many subsequent algorithms.”
Neighborhood rough set based heterogeneous feature subset selection (2008, background): “In numerical spaces, the concept of neighborhood plays an important role [15,27,35,36].”
A survey on unsupervised outlier detection in high‐dimensional numerical data (2012, methodology): “27,28,71,72,75,78,138,148 and LOCI including the approximate aLOCI [102]—also some spatial outlier detection methods such as those in refs.”
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, background): “Connectivity-based outlier factors [155], influenced outlierness [156], and local”
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”
Robust clustering by detecting density peaks and assigning points based on fuzzy weighted K-nearest neighbors (2016, background): “First, if X is the whole dataset of size N , then the KNN distance δK i of a point i is defined by (6) [19] , and the threshold τ is given by (7) (the mean of the KNN distances), then the set of outliers is defined by (8) .”
Ensembles for unsupervised outlier detection: challenges and research questions a position paper (2014, background): “Some popular models are based on the distance between objects [37; 60; 4; 74], or on the density of the neighborhood of an object [9; 56; 34; 38; 42], or based on the variance of angles between object vectors [42; 58], or on other principles of outlierness in various domains [12; 13; 3].”