Varun Chandola

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Vipin KumarUniversity of MinnesotaVerified email at umn.edu
Arindam BanerjeeFounder Professor, Dept of Computer Science, University of Illinois Urbana-ChampaignVerified email at illinois.edu
Ranga Raju VatsavaiCFEP Professor, Computer Science Dept., NCSUVerified email at ncsu.edu
Budhendra BhaduriOak Ridge National LaboratoryVerified email at ornl.gov
Shyam BoriahXylemVerified email at cs.umn.edu
Duc Thanh Anh LuongGlassdoorVerified email at glassdoor.com
Auroop R GangulyCOE Distinguished Professor, Fellow ASCE, Distinguished Member ACM, Northeastern University, BostonVerified email at northeastern.edu
Anil CheriyadatResearch Scientist Oak Ridge National LaboratoryVerified email at ornl.gov
Eddie BrightDistinguished Research Scientist, Oak Ridge National LaboratoryVerified email at ornl.gov
Abani PatraProfessor Mathematics and Computer ScienceVerified email at tufts.edu
Gyorgy SimonMayo Clinic, University of MinnesotaVerified email at umn.edu
Gökhan KulUniversity of Massachusetts DartmouthVerified email at umassd.edu
Shambhu UpadhyayaProfessor of Computer Science and Engineering, University at BuffaloVerified email at buffalo.edu
Jaideep SrivastavaProfessor, University of MinnesotaVerified email at umn.edu
Anthony StefanidisProfessor of Data Science, College of William and MaryVerified email at wm.edu
Oliver A. KennedyAssociate Professor, University at BuffaloVerified email at buffalo.edu
Ting XieDatabricksVerified email at databricks.com
Ryan McManamayBaylor UniversityVerified email at vt.edu
Jibonananda SanyalNational Renewable Energy LaboratoryVerified email at nrel.gov
Robert N StewartScientist, Oak Ridge National Laboratory, Geography Professor, UTKVerified email at ornl.gov

Varun Chandola

Associate Professor, Computer Science Department, University at Buffalo

Verified email at buffalo.edu - Homepage

Data Mining Anomaly Detection Machine Learning


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Anomaly detection: A survey V Chandola, A Banerjee, V Kumar ACM computing surveys (CSUR) 41 (3), 1-58, 2009	15277	2009
Similarity measures for categorical data: A comparative evaluation S Boriah, V Chandola, V Kumar Proceedings of the 2008 SIAM international conference on data mining, 243-254, 2008	874	2008
Anomaly detection for discrete sequences: A survey V Chandola, A Banerjee, V Kumar IEEE transactions on knowledge and data engineering 24 (5), 823-839, 2010	840	2010
Outlier detection: A survey V Chandola, A Banerjee, V Kumar ACM Computing Surveys 14, 15, 2007	502	2007
Comparative evaluation of anomaly detection techniques for sequence data V Chandola, V Mithal, V Kumar 2008 Eighth IEEE international conference on data mining, 743-748, 2008	229	2008
Spatiotemporal data mining in the era of big spatial data: algorithms and applications RR Vatsavai, A Ganguly, V Chandola, A Stefanidis, S Klasky, S Shekhar Proceedings of the 1st ACM SIGSPATIAL international workshop on analytics …, 2012	198	2012
Summarization–compressing data into an informative representation V Chandola, V Kumar Knowledge and Information Systems 12, 355-378, 2007	197	2007
Image based characterization of formal and informal neighborhoods in an urban landscape J Graesser, A Cheriyadat, RR Vatsavai, V Chandola, J Long, E Bright IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2012	193	2012
Knowledge discovery from massive healthcare claims data V Chandola, SR Sukumar, JC Schryver Proceedings of the 19th ACM SIGKDD international conference on Knowledge …, 2013	167	2013
A gaussian process based online change detection algorithm for monitoring periodic time series V Chandola, RR Vatsavai Proceedings of the 2011 SIAM international conference on data mining, 95-106, 2011	89	2011
Detecting anomalies in a time series database V Chandola, D Cheboli, V Kumar	88	2009
Anomaly detection for symbolic sequences and time series data V Chandola University of Minnesota, 2009	85	2009
Similarity metrics for SQL query clustering G Kul, DTA Luong, T Xie, V Chandola, O Kennedy, S Upadhyaya IEEE Transactions on Knowledge and Data Engineering 30 (12), 2408-2420, 2018	61	2018
Data mining for cyber security V Chandola, E Eilertson, L Ertoz, G Simon, V Kumar Data warehousing and data mining techniques for computer security 20, 2006	54	2006
Machine learning for energy-water nexus: challenges and opportunities SMA Zaidi, V Chandola, MR Allen, J Sanyal, RN Stewart, BL Bhaduri, ... Big Earth Data 2 (3), 228-267, 2018	52	2018
Detection of Multi-Step Computer Processes Such as Network Intrusions V Chandola, E Eilertson, H Liu, M Shaneck, C Choi, G Simon, Y Kim, ... US Patent App. 11/794,941, 2008	52	2008
Machine learning approaches for high-resolution urban land cover classification: a comparative study RR Vatsavai, E Bright, C Varun, B Budhendra, A Cheriyadat, J Grasser Proceedings of the 2nd international conference on computing for geospatial …, 2011	46	2011
Anomaly detection: A survey A Banerjee, V Chandola, V Kumar ACM computing surveys 41 (3), 58, 2009	45	2009
Ettu: Analyzing query intents in corporate databases G Kul, D Luong, T Xie, P Coonan, V Chandola, O Kennedy, S Upadhyaya Proceedings of the 25th international conference companion on world wide web …, 2016	43	2016
A scalable gaussian process analysis algorithm for biomass monitoring V Chandola, RR Vatsavai Statistical Analysis and Data Mining: The ASA Data Science Journal 4 (4 …, 2011	42	2011

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