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Biclustering in data mining |
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| Authors: | Stanislav Busygin Oleg Prokopyev Panos M Pardalos |
| Affiliation: | 1. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611, USA;2. Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA |
| 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. |
| Keywords: | Data mining Biclustering Classification Clustering Survey |
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