Algorithms for clustering data
Algorithms for clustering data
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
The Journal of Machine Learning Research
Robust DNA microarray image analysis
Machine Vision and Applications
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
The Latent Process Decomposition of cDNA Microarray Data Sets
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Clustering by soft-constraint affinity propagation
Bioinformatics
A binary variable model for affinity propagation
Neural Computation
Expression microarray classification using topic models
Proceedings of the 2010 ACM Symposium on Applied Computing
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Biclustering of Expression Microarray Data with Topic Models
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
AP-Based Consensus Clustering for Gene Expression Time Series
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
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Biclustering, namely simultaneous clustering of genes and samples, represents a challenging and important research line in the expression microarray data analysis. In this paper, we investigate the use of Affinity Propagation, a popular clustering method, to perform biclustering. Specifically, we cast Affinity Propagation into the Couple Two Way Clustering scheme, which allows to use a clustering technique to perform biclustering. We extend the CTWC approach, adapting it to Affinity Propagation, by introducing a stability criterion and by devising an approach to automatically assemble couples of stable clusters into biclusters. Empirical results, obtained in a synthetic benchmark for biclustering, show that our approach is extremely competitive with respect to the state of the art, achieving an accuracy of 91% in the worst case performance and 100% accuracy for all tested noise levels in the best case.