Other Outstanding Papers
KDD2019
Robust High Dimensional Stream Classification with Novel Class Detection
Zhuoyi WANG (University of Texas at Dallas)*; Zelun Kong (University of Texas at Dallas); Swarup Chandra (University of Texas at Dallas); Hemeng Tao (The University of Texas at Dallas); Latifur Khan (The university of Texas at Dallas)
TARDIS: Distributed Indexing Framework for Big Time Series Data
liang zhang (WPI)*; Noura S Alghamdi (WPI); Mohamed Y. Eltabakh (Worcester Polytechnic Institute); Elke Rundensteiner (WPI)
DBSVEC: Density-Based Clustering Using Support Vector Expansion
Zhen Zohn Wang (Tsinghua University)*; Rui Zhang (” University of Melbourne, Australia”); Jianzhong Qi (The University of Melbourne); Bo Yuan (Tsinghua University)
Adaptive Wavelet Clustering for Highly Noisy Data
Zengjian Chen (Huazhong University of Science and Technology); Jiayi Liu (University of Massachusetts Amherst)*; Yihe Deng (University of California, Los Angeles); Kun He (Huazhong University of Science and Technology); John E Hopcroft (Cornell University)
DBSCAN-MS: Distributed Density-Based Clustering in Metric Spaces
Keyu Yang (Zhejiang University); Yunjun Gao (” Zhejiang University, China”)*; Rui Ma (Zhejiang University); Lu Chen (Aalborg University, Denmark); Sai Wu (Zhejiang Univ); Gang Chen (Zhejiang University)
已读 Deep Anomaly Detection with Deviation Networks
Authors: Guansong Pang (The University of Adelaide);Chunhua Shen (The University of Adelaide);Anton van den Hengel (The University of Adelaide);
dEFEND: Explainable Fake News Detection 没有借鉴性,需要借助新闻的评论来识别
Authors: Kai Shu (Arizona State University);Limeng Cui (The Pennsylvania State University);Suhang Wang (The Pennsylvania State University);Dongwon Lee (Penn State Univeristy);Huan Liu (Arizona State University);
Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach
Authors: Minji Yoon (Carnegie Mellon University);Bryan Hooi (Carnegie Mellon University);Kijung Shin (Carnegie Mellon University);Christos Faloutsos (Carnegie Mellon University);
Sequential Anomaly Detection using Inverse Reinforcement Learning 有很多的关于强化学习的内容,不太适合
Authors: Min-Hwan Oh (Columbia University);Garud Iyengar (Columbia University);
已读 Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network
Authors: Ya Su, Youjian Zhao, Chenhao Niu, Rong Liu, Wei Sun and Dan Pei
Time-Series Anomaly Detection Service at Microsoft
Authors: Hansheng Ren, Bixiong Xu, Yujing Wang, Chao Yi, Congrui Huang, Tony Xing, Xiaoyu Kou, Mao Yang and Jie Tong
Sets2Sets: Learning from Sequential Sets with Neural Networks
Authors: Haoji Hu (University of Minnesota);Xiangnan He (University of Science and Technology of China);
Towards Robust and Discriminative Sequential Data Learning: When and How to Perform Adversarial Training?
这个看起来像是一个有监督的过程,用分类任务做实验。大意是生成一些扰动,让这些扰动附加在原始序列上,然后教导网络去识别出这些扰动。网络挺复杂的
Authors: Xiaowei Jia (University of Minnesota);Sheng Li (University of Georgia);Handong Zhao (Adobe);Sungchul Kim (Adobe);Vipin Kumar (University of Minnesota);
HOLMES: Real-Time APT Detection through Correlation of Suspicious Information Flows 看一下,怎么做实时的
From Anomaly Detection to Rumour Detection using Data Streams of Social Platforms
Thanh Tam Nguyen (Ecole Polytechnique Federale de Lausanne), Matthias Weidlich (Humboldt-Universität zu Berlin), Bolong Zheng (Huazhong University of Science and Technology), Hongzhi Yin (The University of Queensland), Nguyen Quoc Viet Hung (Griffith University), and Bela Stantic (Griffith University)
Efficient Discovery of Sequence Outlier Patterns 好像是做日志的
Lei Cao (MIT), Yizhou Yan (Worcester Polytechnic Institute), Samuel Madden (MIT), Elke Rundensteiner (Worcester Polytechnic Institute), and Mathan Gopalsamy (Signify Research, Cambridge, MA USA)
NETS: Extremely Fast Outlier Detection from a Data Stream via Set-Based Processing
Susik Yoon (KAIST), Jae-Gil Lee (KAIST), and Byung Suk Lee (University of Vermont)
GRAIL: Efficient Time-Series Representation Learning 特复杂,好像还没啥用
John Paparrizos (University of Chicago) and Michael Franklin (University of Chicago)
ICDE2018
Polygraph: A Plug-n-Play Framework to Quantify Anomalies
Yazeed Alabdulkarim (University of Southern California)
Marwan Almaymoni (University of Southern California)
Shahram Ghandeharizadeh (University of Southern California)Efficient Learning Interpretable Shapelets for Accurate Time Series Classification
Zicheng Fang (Fudan University)
Peng Wang (Fudan University)
Wei Wang (Fudan University)Generalized Dynamic Time Warping: Unleashing the Warping Power Hidden in Point-Wise Distances
Rodica Neamtu (Worcester Polytechnic Institute)
Ramoza Ahsan (Worcester Polytechnic Institute)
Elke Rundensteiner (Worcester Polytechnic Institute)
Gabor Sarkozy (Worcester Polytechnic Institute)
Eamonn Keogh (UC Riverside)
Hoang Anh Dau (UC Riverside)
Cuong Nguyen (Worcester Polytechnic Institute)
Charles Lovering (Worcester Polytechnic Institute)Ensemble Direct Density Ratio Estimation for Multistream Classification
Swarup Chandra (University of Texas at Dallas)
Ahsanul Haque (University of Texas at Dallas)
Hemeng Tao (University of Texas at Dallas)
Latifur Khan (University of Texas at Dallas)
Jie Liu (University of Texas at Dallas)
Charu Aggarwal (IBM Research)
ICDE2016
A model-based approach for text clustering with outlier detection. 625-636
A new privacy-preserving solution for clustering massively distributed personal times-series. 1370-1373
Time-series classification with COTE: The collective of transformation-based ensembles. 1548-1549
Fast motif discovery in short sequences. 1158-1169
ICDM2015
Time Series Segmentation to Discover Behavior Switching in Complex Physical Systems
Zheng Han, Lehigh University; Haifeng Chen, NEC Laboratories America; Tan Yan, NEC Laboratories America; Geoff Jiang, NEC Laboratories America
Missing Value Estimation for Hierarchical Time Series: A Study of Hierarchical Web Traffic
Zitao Liu, University of Pittsburgh; Yan Yan, Yahoo! Labs; Milos Hauskrecht, University of Pittsburgh