ACM Computing Surveys (CSUR)
Approximation algorithms
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Approximate clustering via core-sets
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Enhanced Biclustering on Expression Data
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
d-Clusters: Capturing Subspace Correlation in a Large Data Set
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
The maximum edge biclique problem is NP-complete
Discrete Applied Mathematics
Local Search Heuristics for k-Median and Facility Location Problems
SIAM Journal on Computing
Clustering Large Graphs via the Singular Value Decomposition
Machine Learning
Machine Learning
A New Conceptual Clustering Framework
Machine Learning
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A Simple Linear Time (1+ ") -Approximation Algorithm for k-Means Clustering in Any Dimensions
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
Cluster graph modification problems
Discrete Applied Mathematics - Discrete mathematics & data mining (DM & DM)
Clustering via matrix powering
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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 Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation
The Journal of Machine Learning Research
Two-Way Grouping by One-Way Topic Models
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
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
Relational duality: unsupervised extraction of semantic relations between entities on the web
Proceedings of the 19th international conference on World wide web
Approximation algorithms for tensor clustering
ALT'09 Proceedings of the 20th international conference on Algorithmic learning theory
A clustering rule-based approach to predictive modeling
Proceedings of the 48th Annual Southeast Regional Conference
Effective data co-reduction for multimedia similarity search
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Distributed approximate spectral clustering for large-scale datasets
Proceedings of the 21st international symposium on High-Performance Parallel and Distributed Computing
Unsupervised sparse matrix co-clustering for marketing and sales intelligence
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Parameter-less co-clustering for star-structured heterogeneous data
Data Mining and Knowledge Discovery
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
Reduce and aggregate: similarity ranking in multi-categorical bipartite graphs
Proceedings of the 23rd international conference on World wide web
CoBaFi: collaborative bayesian filtering
Proceedings of the 23rd international conference on World wide web
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Co-clustering is the simultaneous partitioning of the rows and columns of a matrix such that the blocks induced by the row/column partitions are good clusters. Motivated by several applications in text mining, market-basket analysis, and bioinformatics, this problem has attracted severe attention in the past few years. Unfortunately, to date, most of the algorithmic work on this problem has been heuristic in nature. In this work we obtain the first approximation algorithms for the co-clustering problem. Our algorithms are simple and obtain constant-factor approximation solutions to the optimum. We also show that co-clustering is NP-hard, thereby complementing our algorithmic result.