期刊
NEUROCOMPUTING
卷 69, 期 16-18, 页码 1882-1896出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2006.02.018
关键词
knowledge discovery; data mining; biclustering; co-clustering; graph drawing; crossing minimization; overlapping biclusters; noise
Clustering only the records in a database (or data matrix) gives a global view of the data. For a detailed analysis or a local view, biclustering or co-clustering is required, involving the clustering of the records and the attributes simultaneously. In this paper, a new graph-drawing-based biclustering technique is proposed based on the crossing minimization paradigm that is shown to work for asymmetric overlapping biclusters in the presence of noise. Both simulated and real world data sets are used to demonstrate the superior performance of the new technique compared with two other conventional biclustering approaches. (c) 2006 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据