62,687 Hits in 8.2 sec

A survey of data mining and social network analysis based anomaly detection techniques

Ravneet Kaur, Sarbjeet Singh
2016 Egyptian Informatics Journal  
The paper presents a review of number of data mining approaches used to detect anomalies.  ...  Anomalous activities in social networks represent unusual and illegal activities exhibiting different behaviors than others present in the same structure.  ...  Clustering approaches are a costly procedure for large data sets Sometimes clustering process involves anomalous objects depicting similar behavior and hence forming the clusters.  ... 
doi:10.1016/j.eij.2015.11.004 fatcat:jixqyc6p5vfx5kkiczcwtt32fy

Where is Waldo (and his friends)? A comparison of anomaly detection algorithms for time-domain astronomy [article]

Juan Rafael Martínez-Galarza, Federica Bianco, Dennis Crake, Kushal Tirumala, Ashish A. Mahabal, Matthew J. Graham, Daniel Giles
2020 arXiv   pre-print
In this paper, we investigate strategies to identify novel objects and to contextualize them within large time-series data sets to facilitate the discovery of new objects, new classes of objects, and the  ...  We compare tree-based and manifold-learning algorithms for anomaly detection as they are applied to a data set of light curves from the Kepler observatory that include the bona fide anomalous Boyajian's  ...  ACKNOWLEDGEMENTS We would like to thank the organizers and participants of the Detecting the Unexpected workshop that took place at STScI in 2017.  ... 
arXiv:2009.06760v1 fatcat:fkekcbd2tfbejkuidarcltngye

Unsupervised detection of contextual anomaly in remotely sensed data

Qi Liu, Rudy Klucik, Chao Chen, Glenn Grant, David Gallaher, Qin Lv, Li Shang
2017 Remote Sensing of Environment  
In this work, we propose an unsupervised anomaly detection framework that requires no prior knowledge and is capable of detecting anomalous events, which we define as groups of outlier objects differing  ...  The techniques and tools developed in this project are generally usable for a diverse set of satellite products and will be made publicly available with the support of the National Snow and Ice Data Center  ...  Acknowledgments This work was supported in part by the National Science Foundation (NSF) under grant No. 1251257.  ... 
doi:10.1016/j.rse.2017.01.034 fatcat:bqwboad4vzaezlmq3awipgwtlq

Anomaly Detection in Astronomical Images with Generative Adversarial Networks [article]

Kate Storey-Fisher, Marc Huertas-Company, Nesar Ramachandra, Francois Lanusse, Alexie Leauthaud, Yifei Luo, Song Huang
2020 arXiv   pre-print
The proposed approach could boost unsupervised discovery in the era of big data astrophysics.  ...  We identify images which are less well-represented in the generator's latent space, and which the discriminator flags as less realistic; these are thus anomalous with respect to the rest of the data.  ...  Acknowledgments and Disclosure of Funding We gratefully acknowledge the Kavli Summer Program in Astrophysics for seeding this project; the initial work was completed at the 2019 program at the University  ... 
arXiv:2012.08082v1 fatcat:nkagipl4nrcw5oxshehsn3pjhq

Using Consensus Clustering for Multi-view Anomaly Detection

Alexander Y. Liu, Dung N. Lam
2012 2012 IEEE Symposium on Security and Privacy Workshops  
This paper presents work on automatically characterizing typical user activities across multiple sources (or views) of data, as well as finding anomalous users who engage in unusual combinations of activities  ...  To avoid detection, these malicious insiders want to appear as normal as possible with respect to the activities of other users with similar privileges and tasks.  ...  Typically, a single set of cluster labels is a mapping from each data point to a group ID, where the goal of most clustering algorithms is to give similar data points (for some measure of similarity) the  ... 
doi:10.1109/spw.2012.18 dblp:conf/sp/LiuL12 fatcat:v2mlakkbufcf5dqyeghx77k7vm

Tracer: Taming Anomalous Events with CRFID Tags for Trajectory Management

Rui Li, Jinsong Han, Zhi Wang, Jizhong Zhao, Yihong Gong, Xiaobin Zhang
2013 International Journal of Distributed Sensor Networks  
Instead of covering the entire processing area, Tracer only deploys RFID readers in essential regions to detect the mishandling, loss, and other abnormal states of items.  ...  Among variant devices for trace detection, computational radio frequency identification (CRFID) tags are promising to draw precise trajectory from the data reported by their accelerometers.  ...  This work is partially supported by the NSFC Major Program under Grant 61190112, the NSFC under Grants 61373175 and 61228202, and the Fundamental Research Funds for the Central Universities of China under  ... 
doi:10.1155/2013/148353 fatcat:dbirvv6zojakhlwixzvy6nbr4y

Hierarchical Probabilistic Models for Group Anomaly Detection

Liang Xiong, Barnabás Póczos, Jeff G. Schneider, Andrew J. Connolly, Jake VanderPlas
2011 Journal of machine learning research  
Often the most interesting or unusual things in a data set are not odd individual points, but rather larger scale phenomena that only become apparent when groups of points are considered.  ...  In this paper, we propose generative models for detecting such group anomalies. We evaluate our methods on synthetic data as well as astronomical data from the Sloan Digital Sky Survey.  ...  Acknowledgements This work was funded in part by the National Science Foundation under grant number NSF-IIS0911032 and the Department of Energy under grant number DESC0002607.  ... 
dblp:journals/jmlr/XiongPSCV11 fatcat:ew3ve5tiqjbshj6usdry2mbz2i

Establishing a many-cytokine signature via multivariate anomaly detection

K. Dingle, A. Zimek, F. Azizieh, A. R. Ansari
2019 Scientific Reports  
a range of anomaly detection algorithms on these data, identifying the best performing methods.  ...  We examine previously published cytokine data sets associated to pregnancy complications, brain tumours, and rheumatoid arthritis, as well as normal healthy control samples, and test the performance of  ...  Acknowledgements We acknowledge funding from the Kuwait Foundation for the Advancement of Sciences (KFAS) Grant Number P115-12SL-06. We also thank the anonymous referees for valuable comments.  ... 
doi:10.1038/s41598-019-46097-9 pmid:31273258 pmcid:PMC6609612 fatcat:uo7tkchgore53ogtq2cqa63sse

Group Anomaly Detection: Past Notions, Present Insights, and Future Prospects

Aqeel Feroze, Ali Daud, Tehmina Amjad, Malik Khizar Hayat
2021 SN Computer Science  
Almost all existing anomaly detection techniques have some limitations and do not focus specifically on detecting anomalous groups.  ...  Anomaly detection is also a crucial problem in processing large-scale datasets when our goal is to find abnormal values or unusual events.  ...  evaluate the detection of anomalous data groups.  ... 
doi:10.1007/s42979-021-00603-x fatcat:oyjzthza7vbhnakpm3t2ko6ctq

Anomalous Behavior Detection in Crowded Scenes Using Clustering and Spatio-Temporal Features [chapter]

Meng Yang, Sutharshan Rajasegarar, Aravinda S. Rao, Christopher Leckie, Marimuthu Palaniswami
2016 IFIP Advances in Information and Communication Technology  
In terms of unsupervised methods, it is hard to obtain a large amount of pre-labelled data from complicated scenes and predict some unanticipated anomalous data.  ...  The anomalous objects loiter on a large scale in PEST2009, whereas objects stand statically or move only slightly in the MCG.  ... 
doi:10.1007/978-3-319-48390-0_14 fatcat:2hscls5eanbgnfzw2iho73jbom


2019 Zenodo  
The similar situation occurs when detecting pre-anomalous situations in the complex high-tech equipment operation.  ...  Usually, logs of large technology platforms represent data sets of very high dimensionality that does not allow modern algorithms in the allowable time limits to draw the necessary conclusions about the  ...  Mean Shift The Mean Shift algorithm groups the objects with similar attributes. Consider an example of the algorithm in the analysis of images.  ... 
doi:10.5281/zenodo.3256472 fatcat:j4zqlwgtwrbfnp7okxurkj7v5y

Anomaly Topic and Emerging Topics Discovery Using Social Media

Yogita P. Shewale, Harshal Kumar R. Khairnar
2019 Helix  
score from various user which are use social medias the data set of social media may be large we need to consider social posts the datasets gathered from Facebook or twitter.  ...  There is large growth in social medias detecting the latest trending topic from social medias links are receiving interest , conventional methods link text mining and text-frequency because the data is  ...  With experimental set up, proposed system proves it's efficiency in terms of accuracy.  ... 
doi:10.29042/2019-4947-4955 fatcat:ltuhxj4tvjbjxckc755jsbc4a4

Detecting Anomalous User Behaviors in Workflow-Driven Web Applications

Xiaowei Li, Yuan Xue, Bradley Malin
2012 2012 IEEE 31st Symposium on Reliable Distributed Systems  
We first decompose web sessions into workflows based on their data objects. In doing so, the detection of anomalous sessions is reduced to detection of anomalous workflows.  ...  The objective of this paper is to detect anomalous user behaviors based on the sequence of their requests within a web session.  ...  The authors would like to thank Dario Giuse for providing the StarPanel traces used in the study and Steve Nyemba for preprocessing the data.  ... 
doi:10.1109/srds.2012.19 dblp:conf/srds/0003XM12 fatcat:6j26a2jvc5axteehvuootxlbnq

ATD: Anomalous Topic Discovery in High Dimensional Discrete Data

Hossein Soleimani, David J. Miller
2016 IEEE Transactions on Knowledge and Data Engineering  
Results of our experiments show that our method can accurately detect anomalous topics and salient features (words) under each such topic in a synthetic data set and two real-world text corpora and achieves  ...  We propose an algorithm for detecting patterns exhibited by anomalous clusters in high dimensional discrete data.  ...  An anomalous cluster is a set of data samples which manifest similar patterns of atypicality.  ... 
doi:10.1109/tkde.2016.2561288 fatcat:qvkzzstwtjfqtft6mngw7bfbwe

Model-free feature selection to facilitate automatic discovery of divergent subgroups in tabular data [article]

Girmaw Abebe Tadesse, William Ogallo, Celia Cintas, Skyler Speakman
2022 arXiv   pre-print
using the whole features with a Jaccard similarity of 0.95 but with a 16x reduction in detection time.  ...  SAFS-selected features are also shown to achieve competitive detection performance, e.g., 18.3% of features selected by SAFS in the Claims dataset detected divergent samples similar to those detected by  ...  This is similar to the group identified using the whole feature set (K = 41), which results in a divergent subgroup of 3078 (16% of whole data) with odds ratio of 3.95 (95% CI: 3.63, 4.31), achieving a  ... 
arXiv:2203.04386v1 fatcat:4dh6a77jzfacppah5gj4tj4k4m
« Previous Showing results 1 — 15 out of 62,687 results