Biclustering in data mining
Biclustering in data mining
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DOI：
10.1016/j.cor.2007.01.005
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发表时间：
2008-09-01
影响因子：
4.6
通讯作者：
Pardalos, Panos M.
中科院分区：
文献类型：
--
作者：
Busygin, Stanislav;Prokopyev, Oleg;Pardalos, Panos M.
双聚类在于将样本集及其属性（特征）集同时划分为子集（类）。分类在一起的样本和特征应该彼此具有高度相关性。在本文中，我们回顾了最广泛使用和成功的双聚类技术及其相关应用。这项调查是从理论角度撰写的，强调现有双聚类技术中可以满足的数学概念。 (c) 2007 年，爱思唯尔有限公司出版。
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.