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PyODDS is an end-to end Python system for outlier detection with database support.
Lof: identifying density-based local outliers
Markus M Breunig, Hans-Peter Kriegel, Raymond T Ng, and Jörg Sander · 2000
Earlier work this paper cites.
Efficient algorithms for mining outliers from large data sets
Sridhar Ramaswamy, Rajeev Rastogi, and Kyuseok Shim · 2000
Earlier work this paper cites.
Estimating the support of a high-dimensional distribution
Bernhard Schölkopf, John C Platt, John Shawe-Taylor, Alex J Smola, and Robert C Williamson · 2001
Earlier work this paper cites.
Outlier detection using replicator neural networks
Simon Hawkins, Hongxing He, Graham Williams, and Rohan Baxter · 2002
Earlier work this paper cites.
Discovering cluster-based local outliers
Zengyou He, Xiaofei Xu, and Shengchun Deng · 2003
Earlier work this paper cites.
Isolation forest
Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou · 2008
Earlier work this paper cites.
Outlier detection in axis-parallel subspaces of high dimensional data
Hans-Peter Kriegel, Peer Kröger, Erich Schubert, and Arthur Zimek · 2009
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Visual evaluation of outlier detection models
Elke Achtert, Hans-Peter Kriegel, Lisa Reichert, Erich Schubert, Remigius Wojdanowski, and Arthur Zimek · 2010
Cited alongside, same era.
RapidMiner: Data mining use cases and business analytics applications
Markus Hofmann and Ralf Klinkenberg · 2013
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Outlier analysis
Charu C Aggarwal · 2015
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Long short term memory networks for anomaly detection in time series
Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, and Puneet Agarwal · 2015
Cited alongside, same era.
Lstm-based encoder-decoder for multi-sensor anomaly detection
Pankaj Malhotra, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, and Gautam Shroff · 2016
PyNomaly: Anomaly detection using local outlier probabilities (LoOP)
Valentino Constantinou · 2018
Later among the works it cites.
Deep autoencoding gaussian mixture model for unsupervised anomaly detection
Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen · 2018
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Graph recurrent networks with attributed random walks
Xiao Huang, Qingquan Song, Yuening Li, and Xia Hu · 2019
Closest in time.
Is a single vector enough? exploring node polysemy for network embedding
Ninghao Liu, Qiaoyu Tan, Yuening Li, Hongxia Yang, Jingren Zhou, and Xia Hu · 2019
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Pyod: A python toolbox for scalable outlier detection
Yue Zhao, Zain Nasrullah, and Zheng Li · 2019
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Cited alongside, same era.
Specae: Spectral autoencoder for anomaly detection in attributed networks, 2019a
Yuening Li, Xiao Huang, Jundong Li, Mengnan Du, and Na Zou
Cited in the paper.
Deep structured cross-modal anomaly detection
Yuening Li, Ninghao Liu, Jundong Li, Mengnan Du, and Xia Hu
Cited in the paper.
A novel anomaly detection scheme based on principal component classifier
Mei-Ling Shyu, Shu-Ching Chen, Kanoksri Sarinnapakorn, and LiWu Chang
Cited in the paper.