News for the Publication Category: IEEE International Conference on Data Mining

New! Detecting Irregular Network Activity with Adversarial Learning and Expert Feedback

Gopikrishna Rathinavel, Nikhil Muralidhar, Naren Ramakrishnan
In Proceedings of the IEEE International Conference on Data Mining, (pp 1161-1166), 02/2023
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New! Robust Regression via Online Feature Selection Under Adversarial Data Corruption

Xuchao Zhang, Shuo Lei, Liang Zhao, Arnold Boediardjo
In Proceedings of the IEEE International Conference on Data Mining, (pp 1440-1445), 12/2018
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New! DeepDiffuse: Predicting the ‘Who’ and ‘When’ in Cascades

Mohammad Raihanul Islam, Sathappan Muthiah, Bijaya Adhikari, Naren Ramakrishnan
In Proceedings of the IEEE International Conference on Data Mining, (pp 1055-1060), 12/2018
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New! A Probabilistic Geographical Aspect-Opinion Model for Geo-Tagged Microblogs

Aman Ahuja, Chandan Reddy
In Proceedings of the IEEE International Conference on Data Mining, (pp 721-726), 12/2017
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New! Learning to Fuse Music Genres with Generative Adversarial Dual Learning

Zhiqian Chen, Yen-Cheng Lu
In Proceedings of the IEEE International Conference on Data Mining, (pp 817-822), 12/2017
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New! Online and Distributed Robust Regressions Under Adversarial Data Corruption

Xuchao Zhang, Liang Zhao, Arnold Boediardjo
In Proceedings of the IEEE International Conference on Data Mining, (pp 625-634), 12/2017
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Feature Grouping Using Weighted l1 Norm for High-Dimensional Data

Chandan Reddy
In Proceedings of the IEEE IEEE International Conference on Data Mining, (pp ), 12/2016
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L-EnsNMF: Boosted Local Topic Discovery via Ensemble of Nonnegative Matrix Factorization

Chandan Reddy
In Proceedings of the IEEE IEEE International Conference on Data Mining, (pp ), 12/2016
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Transfer learning for class imbalance problems with inadequate data

Chandan Reddy
In Proceedings of the IEEE International Conference on Data Mining, (pp ), 12/2016
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Mining Unstable Communities from Network Ensembles


In Proceedings of the IEEE IEEE International Conference on Data Mining, (pp ), 11/2015
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