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
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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