Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data

By Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek. Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2009

320 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Epidemiology

View Paper: https://www.semanticscholar.org/paper/0d91f0fe517786fd668662234ae7c933710aad57

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Citations in the corpus

Outlier Analysis (2013, methodology): “In the context of high-dimensional data, there are two distinct lines of research, one of which investigates the efficiency of high dimensional outlier detection [46, 185, 467], and the other investigates the more fundamental issue of the effectiveness of high dimensional outlier detection [4, 273].”
Ensembles for unsupervised outlier detection: challenges and research questions a position paper (2014, methodology): “On the other hand, using the average of scores has been theoretically advocated [75] for the combination of outlier scores based on (local) density estimates (as used by many classic methods such as LOF and its variants [9; 56; 38; 39] or the kNN outlier model and its variants [60; 4; 74]).”
Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection (2015, methodology): “LoOP [Kriegel et al. 2009a] uses a density estimation based on the distance distribution of all NNs and formulates the local outlier score as a probability.”
Progress in Outlier Detection Techniques: A Survey (2019, methodology): “In most cases, as the data increase in size, the number of outliers also increases [17].”
A survey on unsupervised outlier detection in high‐dimensional numerical data (2012, methodology): “44,45,112,113,129,130,153 and the implementation of refs.”
Interpreting and Unifying Outlier Scores (2011, background): “Adaptations to high dimensional data are, e.g., [12] [32].”
HiCS: High Contrast Subspaces for Density-Based Outlier Ranking (2012, background): “This idea is based on the following key hypothesis: the detection of non-trivial outliers is only possible in a subspace S that shows high dependence between all attributes si ∈ S.”
A comparative evaluation of outlier detection algorithms: Experiments and analyses (2018, background): “The Subspace outlier detection (SOD) [15] algorithm finds for each point p the set of m neighbors shared between p and its k-nearest neighbors.”