Data Mining

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

  1. Discriminating Subsequence Discovery for Sequence Clustering.

    Jianyong Wang, Yuzhou Zhang, Lizhu Zhou, George Karypis, Charu C. Aggarwal. SIAM International Conference on Data Mining (SDM), 2007.

  1. ClustKNN: A Highly Scalable Hybrid Model- and Memory-Based CF Algorithm.

    Al Mamunur Rashid, Shyong K. Lam, George Karypis, and John Riedl. WEBKDD, 2006.

  1. Coherent Closed Quasi-Clique Discovery from Large Dense Graph Databases.

    Zhiping Zeng, Jianyong Wang, Lizhu Zhou, and George Karypis. 12th International Conference on Knowledge and Data Discovery (KDD), 2006.

  1. On Mining Instance-Centric Classification Rules.

    Jianyong Wang and George Karypis. IEEE Transactions on Knowledge and Data Enigneering, 2006.

  1. Building Multiclass Classifiers for Remote Homology Detection and Fold Recognition.

    Huzefa Rangwala and George Karypis. BMC Bioinformatics, Jan 15;23(2):e17-23., 2006.

  1. Acyclic Subgraph-based Descriptor Spaces for Chemical Compound Retrieval and Classification.

    Nikil Wale and George Karypis. IEEE International Conference on Data Mining (ICDM), 2006.

  1. Protein Structure Prediction using String Kernels.

    Huzefa Rangwala, Kevin DeRonne, and George Karypis. UMN CSE 06-005, 2006.

  1. On efficiently summarizing categorical databases.

    Jianyong Wang and George Karypis. Knowledge and Information Systems, vol. 9, no. 1, 2006.

  1. YASSPP: Better Kernels and Coding Schemes Lead to Improvements in SVM-basedSecondary Structure Prediction.

    George Karypis. PROTEINS: Structure, Function, and Bioinformatics, 2006.

  1. Hierarchical Clustering Algorithms for Document Datasets.

    Ying Zhao and George Karypis. Data Mining and Knowledge Discovery, Vol. 10, No. 2, pp. 141 - 168, 2005.

  1. Finding Frequent Patterns in a Large Sparse Graph.

    Michihiro Kuramochi and George Karypis. Data Mining and Knowledge Discovery, 11(3): 243-271, 2005.

  1. Effective Document Clustering for Large Heterogeneous Law Firm Collections.

    Jack G. Conrad, Khalid Al-Kofahi, Ying Zhao, and George Karypis. 10th International Conference on Artificial Intelligence and Law (ICAIL), pp. 177-187, 2005.