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Data Mining

Here is a list of publications that are related to our data mining research.

  1. MPI for Big Data: New Tricks for an Old Dog.

    Dominique LaSalle and George Karypis. Parallel Computing, 2014.

  1. L2AP: Fast Cosine Similarity Search With Prefix L-2 Norm Bounds.

    David C. Anastasiu and George Karypis. 30th IEEE International Conference on Data Engineering (ICDE), 2014.

  1. A Versatile Graph-based Approach to Package Recommentation.

    Roberto Interdonato, Salvatore Romeo, Andrea Tagarelli, and George Karypis. IEEE International Conference on Tools with Artificial Intelligence, 2013.

  1. FISM: Factored Item Similarity Models for Top-N Recommender Systems.

    Santosh Kabbur, Xia Ning, and George Karypis. 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2013.

  1. AREM: A Novel Associative Regression Model Based on the EM Algorithm.

    Zhonghua Jiang and George Karypis. 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2013.

  1. Document Clustering: The Next Frontier.

    David C. Anastasiu and Andrea Tagarelli and George Karypis. UMN-CS&E TR 13-004, 2013.

  1. Coarse- and fine-grained models for proteins: evaluation by decoy discrimination.

    Chris Kauffman and George Karypis. Proteins, 2013.

  1. Multivariate analysis of cell culture bioprocess data--lactate consumption as process indicator.

    Huong Le and Santosh Kabbur and Luciano Pollastrini and Ziran Sun and Keri Mills and Kevin Johnson and George Karypis and Wei-Shou Hu. Journal of Biotechnology, 162, pp. 210-223, 2012.

  1. A segment-based approach to clustering multi-topic documents.

    Andrea Tagarelli and George Karypis. Knowledge and Information Systems, Sptember, 2012.

  1. Multi-view learning via probabilistic latent semantic analysis .

    Fuzhen Zhuang, George Karypis, Xia Ning, Qing He, and Zhongzhi Shi. Information Sciences, 199; pp. 20-30, 2012.

  1. Improved Machine Learning Models for Predicting Selective Compounds.

    Xia Ning and George Karypis. Journal of Chemical Information and Modeling, 58, pp. 38-50, 2012.

  1. Sparse Linear Methods with Side Information for Top-N Recommendations.

    Xia Ning and George Karypis. 6th ACM Recommender Systems Conference (RecSys), 2012.