Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
A Data-Clustering Algorithm on Distributed Memory Multiprocessors
Revised Papers from Large-Scale Parallel Data Mining, Workshop on Large-Scale Parallel KDD Systems, SIGKDD
P-AutoClass: Scalable Parallel Clustering for Mining Large Data Sets
IEEE Transactions on Knowledge and Data Engineering
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A Scalable Collaborative Filtering Framework Based on Co-Clustering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
ICDCS '06 Proceedings of the 26th IEEE International Conference on Distributed Computing Systems
A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation
The Journal of Machine Learning Research
Evaluating MapReduce for Multi-core and Multiprocessor Systems
HPCA '07 Proceedings of the 2007 IEEE 13th International Symposium on High Performance Computer Architecture
Coclustering of Human Cancer Microarrays Using Minimum Sum-Squared Residue Coclustering
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Distributed scalable collaborative filtering algorithm
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part I
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Co-clustering has been extensively used in varied applications because of its potential to discover latent local patterns that are otherwise unapparent by usual unsupervised algorithms such as k-means Recently, a unified view of co-clustering algorithms, called Bregman co-clustering (BCC), provides a general framework that even contains several existing co-clustering algorithms, thus we expect to have more applications of this framework to varied data types However, the amount of data collected from real-life application domains easily grows too big to fit in the main memory of a single processor machine Accordingly, enhancing the scalability of BCC can be a critical challenge in practice To address this and eventually enhance its potential for rapid deployment to wider applications with larger data, we parallelize all the twelve co-clustering algorithms in the BCC framework using message passing interface (MPI) In addition, we validate their scalability on eleven synthetic datasets as well as one real-life dataset, where we demonstrate their speedup performance in terms of varied parameter settings.