Predictive discrete latent factor models for large scale dyadic data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for simultaneous co-clustering and learning from complex data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Approximation algorithms for co-clustering
Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Coclustering of Human Cancer Microarrays Using Minimum Sum-Squared Residue Coclustering
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
CONSENSUS-BASED ENSEMBLES OF SOFT CLUSTERINGS
Applied Artificial Intelligence
A scalable framework for discovering coherent co-clusters in noisy data
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Parameter-Free Hierarchical Co-clustering by n-Ary Splits
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Improving document clustering in a learned concept space
Information Processing and Management: an International Journal
Relational duality: unsupervised extraction of semantic relations between entities on the web
Proceedings of the 19th international conference on World wide web
I/O scalable Bregman co-clustering
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
MIB: Using mutual information for biclustering gene expression data
Pattern Recognition
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Approximation algorithms for tensor clustering
ALT'09 Proceedings of the 20th international conference on Algorithmic learning theory
SCOAL: A framework for simultaneous co-clustering and learning from complex data
ACM Transactions on Knowledge Discovery from Data (TKDD)
Incremental collaborative filtering via evolutionary co-clustering
Proceedings of the fourth ACM conference on Recommender systems
PAC-Bayesian Analysis of Co-clustering and Beyond
The Journal of Machine Learning Research
Distributed scalable collaborative filtering algorithm
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part I
On context-aware co-clustering with metadata support
Journal of Intelligent Information Systems
Scalable co-clustering algorithms
ICA3PP'10 Proceedings of the 10th international conference on Algorithms and Architectures for Parallel Processing - Volume Part I
Data transformation for sum squared residue
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
International Journal of Intelligent Systems
Detecting communities in K-partite K-uniform (hyper)networks
Journal of Computer Science and Technology - Special issue on Community Analysis and Information Recommendation
Constrained co-clustering with non-negative matrix factorisation
International Journal of Business Intelligence and Data Mining
Situation-Aware on mobile phone using co-clustering: algorithms and extensions
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
SPCF: a stepwise partitioning for collaborative filtering to alleviate sparsity problems
Journal of Information Science
A unified adaptive co-identification framework for high-d expression data
PRIB'12 Proceedings of the 7th IAPR international conference on Pattern Recognition in Bioinformatics
Parameter-less co-clustering for star-structured heterogeneous data
Data Mining and Knowledge Discovery
Social event detection with robust high-order co-clustering
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
CopyCatch: stopping group attacks by spotting lockstep behavior in social networks
Proceedings of the 22nd international conference on World Wide Web
Hierarchical co-clustering: off-line and incremental approaches
Data Mining and Knowledge Discovery
Expert Systems with Applications: An International Journal
A Probabilistic Latent Semantic Analysis Model for Coclustering the Mouse Brain Atlas
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Co-clustering, or simultaneous clustering of rows and columns of a two-dimensional data matrix, is rapidly becoming a powerful data analysis technique. Co-clustering has enjoyed wide success in varied application domains such as text clustering, gene-microarray analysis, natural language processing and image, speech and video analysis. In this paper, we introduce a partitional co-clustering formulation that is driven by the search for a good matrix approximation---every co-clustering is associated with an approximation of the original data matrix and the quality of co-clustering is determined by the approximation error. We allow the approximation error to be measured using a large class of loss functions called Bregman divergences that include squared Euclidean distance and KL-divergence as special cases. In addition, we permit multiple structurally different co-clustering schemes that preserve various linear statistics of the original data matrix. To accomplish the above tasks, we introduce a new minimum Bregman information (MBI) principle that simultaneously generalizes the maximum entropy and standard least squares principles, and leads to a matrix approximation that is optimal among all generalized additive models in a certain natural parameter space. Analysis based on this principle yields an elegant meta algorithm, special cases of which include most previously known alternate minimization based clustering algorithms such as kmeans and co-clustering algorithms such as information theoretic (Dhillon et al., 2003b) and minimum sum-squared residue co-clustering (Cho et al., 2004). To demonstrate the generality and flexibility of our co-clustering framework, we provide examples and empirical evidence on a variety of problem domains and also describe novel co-clustering applications such as missing value prediction and compression of categorical data matrices.