Outlier Analysis

By Charu C. Aggarwal. Springer: New York, 2013

1,746 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/1bc042ec7a58ca8040ee08178433752f2c16f25e

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In Artificial Intelligence

In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Neural Networks and Deep Learning (2018, neutral): “A later book [5] discusses how this approach can be combined with a variance reduction method.”
PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds (2019, background): “…classical approaches for outlier detection, classiﬁcation and removal have been proposed primarily in the statistics and data mining communities, in the general setting of point clouds in arbitrary dimensions, with several monographs dedicated speciﬁcally to this topic [Pin95,BG05,RH11,Agg15].”
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].”
Data Cleaning: Overview and Emerging Challenges (2016, background): “Quantitative data cleaning techniques have been extensively covered in multiple surveys [2, 65, 40] and tutorials [48, 17], but there have been fewer surveys of qualitative data cleaning [44].”
Deep One-Class Classification (2018, background): “Anomaly detection (AD) (Chandola et al., 2009; Aggarwal, 2016) is the task of discerning unusual samples in data.”
Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines (2021, background): “To the best of our knowledge, previous reviews [1], [2], [9]–[14] on this subject matter do no more than simply categorize models according to their mechanisms and describe their characteristics.”
Outlier Detection in Heterogeneous Datasets using Automatic Tuple Expansion (2016, background): “There has been substantial research in how to build models to detect outliers [2], including how to detect outliers in high-dimensional data by searching the subspaces of the data [23][12].”
Robust Random Cut Forest Based Anomaly Detection on Streams (2016, background): “We survey some of the work here; for an extensive survey see (Aggarwal, 2013; Chandola et al., 2009) and references therein.”
MacroBase: Analytic Monitoring for the Internet of Things (2016, background): “Anomaly and outlier detection methods date to at least the 19th century; the literature contains thousands of techniques from communities including statistics, machine learning, data mining, and information theory [4, 10, 26].”
Anomaly Detection using One-Class Neural Networks (2018, background): “Anomaly detection is a well-studied topic in Data Science Chandola et al. [2007], Aggarwal [2016].”
PointASNL: Robust Point Clouds Processing Using Nonlocal Neural Networks With Adaptive Sampling (2020, methodology): “etwork gradually become confused without a hierarchical structure. Outlier Removal and Sampling Strategy. Outliers and noise usually exist in raw point clouds data. Previous robust statistics methods [1] for outlier removal suffer from non-trivial parameter tuning or require additional information [”
Radar: Residual Analysis for Anomaly Detection in Attributed Networks (2017, background): “outlier detection) [Chandola et al., 2009; Aggarwal, 2015] aims to discover rare instances that do not conform to the patterns of majority.”