Global monitoring of inland water dynamics: State-of-the-art, challenges, and opportunities A Karpatne, A Khandelwal, X Chen, V Mithal, J Faghmous, V Kumar Computational sustainability, 121-147, 2016 | 93 | 2016 |
Contextual time series change detection XC Chen, K Steinhaeuser, S Boriah, S Chatterjee, V Kumar Proceedings of the 2013 SIAM International Conference on Data Mining, 503-511, 2013 | 32 | 2013 |
Clustering dynamic spatio-temporal patterns in the presence of noise and missing data X Chen, JH Faghmous, A Khandelwal, V Kumar Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015 | 25 | 2015 |
A new data mining framework for forest fire mapping XC Chen, A Karpatne, Y Chamber, V Mithal, M Lau, K Steinhaeuser, ... 2012 Conference on Intelligent Data Understanding, 104-111, 2012 | 14 | 2012 |
Earth science applications of sensor data A Karpatne, J Faghmous, J Kawale, L Styles, M Blank, V Mithal, X Chen, ... Managing and Mining Sensor Data, 505-530, 2013 | 9 | 2013 |
A study of time series noise reduction techniques in the context of land cover change detection X Chen, V Mithal, S VangalaReddy, I Brugere, S Boriah, V Kumar | 7 | 2011 |
Computational Sustainability A Karpatne, A Khandelwal, X Chen, V Mithal, J Faghmous, V Kumar Lässig, J., Kersting, K., Morik, K., Eds, 121-147, 2016 | 5 | 2016 |
Online discovery of group level events in time series XC Chen, A Mueen, VK Narayanan, N Karampatziakis, G Bansal, ... Proceedings of the 2014 SIAM International Conference on Data Mining, 632-640, 2014 | 5 | 2014 |
Unsupervised method for water surface extent monitoring using remote sensing data XC Chen, A Khandelwal, S Shi, JH Faghmous, S Boriah, V Kumar Machine Learning and Data Mining Approaches to Climate Science: Proceedings …, 2015 | 4 | 2015 |
Identifying dynamic changes with noisy labels in spatial-temporal data: A study on large-scale water monitoring application X Jia, X Chen, A Karpatne, V Kumar 2016 IEEE International Conference on Big Data (Big Data), 1328-1333, 2016 | 3 | 2016 |
A general framework to increase the robustness of model-based change point detection algorithms to outliers and noise XC Chen, Y Yao, S Shi, S Chatterjee, V Kumar, JH Faghmous Proceedings of the 2016 SIAM International Conference on Data Mining, 162-170, 2016 | 3 | 2016 |
Yashu Chamber, Varun Mithal, Michael Lau, Karsten Steinhaeuser, Shyam Boriah, Michael Steinbach, Vipin Kumar, Christopher S Potter, et al. A new data mining framework for … XC Chen, A Karpatne Conference on Intelligent Data Understanding (CIDU), 104-111, 2012 | 3 | 2012 |
Unsupervised framework to monitor lake dynamics S Boriah, V Kumar, A Khandelwal, XC Chen US Patent 9,430,839, 2016 | 2 | 2016 |
Online change detection algorithm for noisy time-series: An application tonear-real time burned area mapping XC Chen, V Kumar, JH Faghmous 2015 IEEE International Conference on Data Mining Workshop (ICDMW), 1536-1537, 2015 | 2 | 2015 |
Unsupervised methods to discover events from spatio-temporal data X Chen University of Minnesota, 2017 | | 2017 |
Supplement for" Contextual Time Series Change Detection" X Chen, K Steinhaeuser, S Boriah, S Chatterjee, V Kumar | | 2013 |
Contextual Time Series Change Detection for Earth Science Data XC Chen, K Steinhaeuser, S Boriah, S Chatterjee, V Kumar AGU Fall Meeting Abstracts 2012, GC51B-1189, 2012 | | 2012 |
A Data Mining Framework for Forest Fire Mapping A Karpatne, X Chen, Y Chamber, V Mithal, M Lau, K Steinhaeuser, ... | | 2012 |
ONLINE CHANGE POINT DETECTION FOR REMOTE SENSING TIME SERIES XC Chen, Y Yao, S Shi, V Kumar, JH Faghmous | | |