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Biclustering in data mining
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DOI：10.1016/j.cor.2007.01.005.png)
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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. (c) 2007 Published by Elsevier Ltd.
Keywords:
data mining
biclustering
classification
clustering
survey
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