Publications

• Bayesian Co-clustering
  H. Shan, A. Banerjee.
  IEEE International Conference on Data Mining (ICDM), (2008).
  
  
• Multiplicative Mixture Models for Overlapping Clustering
  Q. Fu, A. Banerjee.
  IEEE International Conference on Data Mining (ICDM), (2008).
  
  
• A Social Query Model for Distributed Search
  A. Banerjee, S. Basu.
  2nd ACM Workshop on Social Network Mining and Analysis (SNAKDD), (2008) (pdf).
  
  
• Social Topic Models for Community Extraction
  N. Pathak, C. DeLong, K. Erickson, A. Banerjee.
  2nd ACM Workshop on Social Network Mining and Analysis (SNAKDD), (2008).
  
  
• Clustering with Balancing Constraints
  A. Banerjee, J. Ghosh.
  Constrained Clustering: Advances in Algorithms, Theory, and Applications, CRC Press, (2008).
  
  
• Meta-prediction of phosphorylation sites with weighted voting and restricted grid search parameter selection
  J. Wan, S. Kang, C. Tang, J. Yan, Y. Ren, J. Liu, X. Gao, A. Banerjee, L. Ellis, T. Li.
  Nucleic Acids Research (NAR), (2008).
  
  
• I/O Scalable Bregman Clustering
  K. Hsu, A. Banerjee, J. Srivastava.
  Pacific-Asian Conference on Knowledge Discovery and Data Mining (PAKDD), (2008).
  
  
• A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation
  A. Banerjee, I. Dhillon, J. Ghosh, S. Merugu, D. Modha.
  Journal of Machine Learning Research (JMLR), (2007), (pdf).
  
  
  • Initial ideas for this paper appeared as:
    A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation
    A. Banerjee, I. Dhillon, J. Ghosh, S. Merugu, D. Modha.
    International Conference on Knowledge Discovery and Data Mining (KDD) (2004).
  
  
• Latent Dirichlet Conditional Naive Bayes Models
  A. Banerjee and H. Shan.
  IEEE International Conference on Data Mining (ICDM) (2007) (pdf).
• Anomaly Detection in Transportation Corridors using Manifold Embedding
  A. Agovic, A. Banerjee, A. Ganguly, and V. Protopopescu.
  1st International Workshop on Knowledge Discovery from Sensor Data (Sensor-KDD) (2007).
• An Analysis of Logistic Models: Exponential Family Connections and Online Performance
  A. Banerjee.
  SIAM International Conference on Data Mining (SDM) (2007) (pdf).
• Multi-way Clustering on Relation Graphs
  A. Banerjee, S. Basu, S. Merugu.
  SIAM International Conference on Data Mining (SDM) (2007) (pdf).
• Topic Models over Text Streams: A Study of Batch and Online Unsupervised Learning
  A. Banerjee, S. Basu.
  SIAM International Conference on Data Mining (SDM) (2007) (pdf, Longer version).
• On Bayesian Bounds
  A. Banerjee.
  International Conference on Machine Learning (ICML) (2006) (pdf).
  
  
• Scalable Clustering with Balancing Constraints
  A. Banerjee and J. Ghosh.
  Data Mining and Knowledge Discovery (2006) (pdf).
  
  
• Probabilistic Semi-supervised Clustering with Constraints
  S. Basu, M. Bilenko, A. Banerjee, and R. Mooney
  Semi-Supervised Learning MIT Press, (2006).
  
  
• A Clustering Based Approach to Perceptual Image Hashing
  V. Monga, A. Banerjee and B. Evans.
  IEEE Transactions on Information Forensics and Security (2006).
  
  
• Model Based Overlapping Clustering
  A. Banerjee, C. Krumpelman, S. Basu, R. Mooney and J. Ghosh.
  International Conference on Knowledge Discovery and Data Mining (KDD) (2005) (ps,pdf).
  
  
• Clustering with Bregman Divergences
  A. Banerjee, S. Merugu, I. Dhillon and J. Ghosh.
  Journal of Machine Learning Research (JMLR) (2005).
  
  
  • Initial version appeared as:
    Clustering with Bregman Divergences
    A. Banerjee, S. Merugu, I. Dhillon and J. Ghosh.
    SIAM International Conference on Data Mining (SDM) (2004) (ps, pdf).
  
  
• Clustering on the Unit Hypersphere using Von Mises-Fisher Distributions
  A. Banerjee, I. Dhillon, J. Ghosh and S. Sra.
  Journal of Machine Learning Research (JMLR) (2005) (pdf).
  
  
  • Initial version appeared as:
    Generative Model-based Clustering of Directional Data
    A. Banerjee, I. Dhillon, J. Ghosh and S. Sra.
    International Conference on Knowledge Discovery and Data Mining (KDD) (2003) (pdf).
  
  
• On the Optimality of Conditional Expectation as a Bregman Predictor
  A. Banerjee, X. Guo and H. Wang.
  IEEE Transactions on Information Theory, 51(7), 2664-2669 (2005) (pdf).
  
  
  • A shorter version appeared as:
    Optimal Bregman Prediction and Jensen's Equality
    A. Banerjee, X. Guo and H. Wang.
    IEEE International Symposium on Information Theory (ISIT) (2004) (pdf).
  
  
• An Objective Evaluation Crietrion for Clustering
  A. Banerjee and J. Langford.
  International Conference on Knowledge Discovery and Data Mining (KDD) (2004) (ps).
  
  
• An Information Theoretic Analysis of Maximum Likelihood Mixture Estimation for Exponential Families
  A. Banerjee, I. Dhillon, J. Ghosh and S. Merugu.
  International Conference on Machine Learning (ICML) (2004). (ps).
  
  
  • An extended abstract of this paper appeared as:
    Rate Distortion, Bregman Divergences and Maximum Likelihood Mixture Estimation
    A. Banerjee, I. Dhillon, J. Ghosh and S. Merugu.
    The Learning Workshop at Snowbird (2004).
  
  
• Active Semi-supervision for Pairwise Constrained Clustering
  S. Basu, A. Banerjee and R. Mooney.
  SIAM International Conference on Data Mining (SDM) (2004) (pdf).
  
  
• Mean Model Clustering
  A. Banerjee, and J. Ghosh. The Learning Workshop at Snowbird (2003) (ps).
  
  
• Frequency Sensitive Competitive Learning for Balanced Clustering on High-dimensional Hyperspheres
  A. Banerjee, and J. Ghosh.
  IEEE Transactions on Neural Networks (2004) (ps).
  
  
  • Initial ideas for this paper appeared as:
    Frequency Sensitive Competitive Learning for Clustering on High Dimensional Hyperspheres
    A. Banerjee, and J. Ghosh.
    Proceedings of the International Joint Conference on Neural Networks (IJCNN) (2002) (ps, pdf).
    
    
  • Extensions to streaming data appeared as:
    Competitive Learning Mechanisms for Scalable, Incremental and Balanced Clustering of Streaming Texts
    A. Banerjee, and J. Ghosh.
    International Joint Conference on Neural Networks(IJCNN): Special Session on Incremental Learning (2003).
  
  
• Semi-supervised Clustering by Seeding
  S. Basu, A. Banerjee, and R. Mooney.
  Proceedings of the International Conference on Machine Learning (ICML) (2002) (ps, pdf).
  
  
• On Scaling Up Balanced Clustering Algorithms
  A. Banerjee, and J. Ghosh.
  Proceedings of the 2nd SIAM International Conference on Data Mining (SDM) (2002) (ps, pdf).
  
  
• Characterizing Visitors to a Website Across Multiple Sessions
  A. Banerjee and J. Ghosh.
  Proceedings of the National Science Foundation(NSF) Workshop on Next Generation Data Mining (2002).
  
  
• Clickstream Clustering using Weighted Longest Common Subsequence
  A. Banerjee, and J. Ghosh.
  Proceedings of the 1st SIAM International Conference on Data Mining: Workshop on Web Mining (2001) (ps, pdf).
  
  
• Concept-based Clustering of Clickstream Data
  A. Banerjee, and J. Ghosh.
  Proceedings of the 3rd International Conference on Information Technology, pp 145-160 (2000).
  
  
• Computerized Tumor Boundary Detection Using Genetic Algorithm
  A. Banerjee.
  Proceedings of the National Conference on Applications of Signal Processing, India (1998).