Data Mining
Here is a list of publications that are related to our data mining research.
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Feature-based factorized Bilinear Similarity Model for Cold-Start Top-n Item Recommendation.
Mohit Sharma, Jiayu Zhou, Junling Hu, and George Karypis. 2015 SIAM International Conference on Data Mining, 2015.
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A comprehensive survey of neighborhood-based recommendation methods.
Xia Ning, Christian Desrosiers, and George Karypis. In Recommender Systems Handbook; 2nd edition, 2015.
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Understanding Computer Usage Evolution.
David C. Anastasiu, Al M. Rashid, Andrea Tagarelli, and George Karypis. 31st IEEE International Conference on Data Engineering (ICDE), 2015.
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NLMF: NonLinear Matrix Factorization Methods for Top-N Recommender Systems.
Santosh Kabbur and George Karypis. 7th ICDM International Workshop on Domain Driven Data Mining (DDDM), 2014.
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Memory-Efficient Parallel Computation of Tensor and Matrix Products for Big Tensor Decomposition.
Niranjay Ravindran, Nicholas D. Sidiropoulos, Shaden Smith, and George Karypis. 28th Asilomar Conference on Signals, 2014.
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HOSLIM: Higher-Order Sparse Linear Method for Top-N Recommender Systems.
Evangelia Christakopoulou and George Karypis. 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2014.
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Signaling Adverse Drug Reactions with Novel Feature-based Similarity Model.
Fan Yang, Xiaohui Yu, and George Karypis. IEEE Conf. on Bioinformatics and Biomedicine (BIBM), 2014.
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Big Data Frequent Pattern Mining.
David C. Anastasiu, Jeremy Iverson, Shaden Smith, and George Karypis. Frequent Pattern Mining (editors: Charu C. Aggarwal and Jawei Han), Springer, 2014.
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Multi-threaded Modularity Based Graph Clustering using the Multilevel Paradigm.
Dominique LaSalle and George Karypis. Journal of Parallel and Distributed Computing, 2014.
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