Bi-clustering gene expression data using co-similarity
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
An architecture to efficiently learn co-similarities from multi-view datasets
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
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Co-clustering has been defined as a way to organize simultaneously subsets of instances and subsets of features in order to improve the clustering of both of them. In previous work, we proposed an efficient co-similarity measure allowing to simultaneously compute two similarity matrices between objects and features, each built on the basis of the other. Here we propose a generalization of this approach by introducing a notion of pseudo-norm and a pruning algorithm. Our experiments show that this new algorithm significantly improves the accuracy of the results when using either supervised or unsupervised feature selection data and that it outperforms other algorithms on various corpora.