Biclustering in data mining

Abstract Biclustering consists in simultaneous partitioning of the set of samples and the set of their attributes (features) into subsets...

Abstract

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Biclustering consists in simultaneous partitioning of the set of samples and the set of their attributes (features) into subsets
(classes). Samples and features classified together are supposed to have a high relevance to each other. In this paper we review the

most widely used and successful biclustering techniques and their related applications. This survey is written from a theoretical
viewpoint emphasizing mathematical concepts that can be met in existing biclustering techniques.

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