Publications Related to Data mining

  1. Accounting for Language Changes over Time in Document Similarity Search.

    Sara Morsy and George Karypis. UMN-CS TR 15-011, 2015.

  1. SPLATT: Efficient and Parallel Sparse Tensor-Matrix Multiplication.

    Shaden Smith, Niranjay Ravindran, Nicholas D. Sidiropoulos, and George Karypis. 29th IEEE International Parallel & Distributed Processing Symposium, 2015.

  1. Mining Coevolving Induced Relational Motifs in Dynamic Networks.

    Rezwan Ahmed and George Karypis. Workshop on Dynamic Networks (SDM-Networks), SIAM Data mining Conference, 2015.

  1. 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.

  1. Understanding Computer Usage Evolution.

    David C. Anastasiu, Al M. Rashid, Andrea Tagarelli, and George Karypis. 31st IEEE International Conference on Data Engineering (ICDE), 2015.

  1. 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.

  1. 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.

  1. 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.

  1. Algorithms for Mining the Coevolving Relational Motifs in Dynamic Networks.

    Rezwan Ahmed and George Karypis. UMN CS 14-008, 2014.

  1. 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.

  1. Personalized Multi-Regression Models for Predicting Students' Performance in Course Activities.

    Asmaa Elbadrawy, R. Scott Studham, and George Karypis. UMN CS 14-011, 2014.

  1. User-Specific Feature-based Similarity Models for Top-N Recommendation of New Items.

    Asmaa Elbadrawy and George Karypis. UMN CS 14-016, 2014.