Combinatorica
Isoperimetric numbers of graphs
Journal of Combinatorial Theory Series B
OHSUMED: an interactive retrieval evaluation and new large test collection for research
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Recent directions in netlist partitioning: a survey
Integration, the VLSI Journal
Applied numerical linear algebra
Applied numerical linear algebra
On the Quality of Spectral Separators
SIAM Journal on Matrix Analysis and Applications
Geometric Mesh Partitioning: Implementation and Experiments
SIAM Journal on Scientific Computing
ACM Computing Surveys (CSUR)
Document Categorization and Query Generation on the World Wide WebUsing WebACE
Artificial Intelligence Review - Special issue on data mining on the Internet
Document clustering using word clusters via the information bottleneck method
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Bipartite graph partitioning and data clustering
Proceedings of the tenth international conference on Information and knowledge management
Correlating multilingual documents via bipartite graph modeling
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Which Eigenvalues Are Found by the Lanczos Method?
SIAM Journal on Matrix Analysis and Applications
A matrix density based algorithm to hierarchically co-cluster documents and words
WWW '03 Proceedings of the 12th international conference on World Wide Web
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A graph-theoretic approach to extract storylines from search results
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A generalized maximum entropy approach to bregman co-clustering and matrix approximation
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Image and Feature Co-Clustering
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Co-clustering by block value decomposition
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Unsupervised content discovery in composite audio
Proceedings of the 13th annual ACM international conference on Multimedia
A Scalable Collaborative Filtering Framework Based on Co-Clustering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Isoperimetric Graph Partitioning for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Isoperimetric Partitioning: A New Algorithm for Graph Partitioning
SIAM Journal on Scientific Computing
Co-clustering Documents and Words Using Bipartite Isoperimetric Graph Partitioning
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Narrowing the semantic gap - improved text-based web document retrieval using visual features
IEEE Transactions on Multimedia
Clustering high dimensional data: A graph-based relaxed optimization approach
Information Sciences: an International Journal
Multi-way clustering and biclustering by the Ratio cut and Normalized cut in graphs
Journal of Combinatorial Optimization
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Data co-clustering refers to the problem of simultaneous clustering of two data types. Typically, the data is stored in a contingency or co-occurrence matrix C where rows and columns of the matrix represent the data types to be co-clustered. An entry C ij of the matrix signifies the relation between the data type represented by row i and column j. Co-clustering is the problem of deriving sub-matrices from the larger data matrix by simultaneously clustering rows and columns of the data matrix. In this paper, we present a novel graph theoretic approach to data co-clustering. The two data types are modeled as the two sets of vertices of a weighted bipartite graph. We then propose Isoperimetric Co-clustering Algorithm (ICA)--a new method for partitioning the bipartite graph. ICA requires a simple solution to a sparse system of linear equations instead of the eigenvalue or SVD problem in the popular spectral co-clustering approach. Our theoretical analysis and extensive experiments performed on publicly available datasets demonstrate the advantages of ICA over other approaches in terms of the quality, efficiency and stability in partitioning the bipartite graph.