| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 475987 | Computers & Operations Research | 2008 | 24 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Stanislav Busygin, Oleg Prokopyev, Panos M. Pardalos,