Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Discovering local structure in gene expression data: the order-preserving submatrix problem
Proceedings of the sixth annual international conference on Computational biology
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
A divisive information theoretic feature clustering algorithm for text classification
The Journal of Machine Learning Research
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Simultaneous Feature Selection and Clustering Using Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Mining Shifting-and-Scaling Co-Regulation Patterns on Gene Expression Profiles
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
BicAT: a biclustering analysis toolbox
Bioinformatics
Bregman Bubble Clustering: A Robust, Scalable Framework for Locating Multiple, Dense Regions in Data
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation
The Journal of Machine Learning Research
Coclustering of Human Cancer Microarrays Using Minimum Sum-Squared Residue Coclustering
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Mining Approximate Order Preserving Clusters in the Presence of Noise
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Mining for the most certain predictions from dyadic data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A knowledge-driven bi-clustering method for mining noisy datasets
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Social event detection with robust high-order co-clustering
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
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Clustering problems often involve datasets where only a part of the data is relevant to the problem, e.g., in microarray data analysis only a subset of the genes show cohesive expressions within a subset of the conditions/features. The existence of a large number of non-informative data points and features makes it challenging to hunt for coherent and meaningful clusters from such datasets. Additionally, since clusters could exist in different subspaces of the feature space, a co-clustering algorithm that simultaneously clusters objects and features is often more suitable as compared to one that is restricted to traditional "one-sided" clustering. We propose Robust Overlapping Co-Clustering (ROCC), a scalable and very versatile framework that addresses the problem of efficiently mining dense, arbitrarily positioned, possibly overlapping co-clusters from large, noisy datasets. ROCC has several desirable properties that make it extremely well suited to a number of real life applications.