Hybrid hierarchical clustering with applications to microarray data
Hybrid hierarchical clustering with applications to microarray data
复制标题
DOI：
10.1093/biostatistics/kxj007
复制
发表时间：
2006-04-01
期刊：
影响因子：
2.1
通讯作者：
Tibshirani, R
中科院分区：
文献类型：
--
作者：
Chipman, H;Tibshirani, R
在本文中，我们提出了一种混合聚类方法，结合了自底向上的层次聚类和自顶向下的聚类的优势。第一种方法擅长识别小集群，但不擅长识别大集群;第二种方法的优势正好相反。混合方法建立在相互聚类的新思想上：一组点彼此之间比任何其他点都更接近。相互集群和自下而上的聚类方法之间的理论联系的建立，帮助他们的解释，并提供一个算法识别的相互集群。我们说明了模拟和真实的微阵列数据集的技术。
In this paper, we propose a hybrid clustering method that combines the strengths of bottom-up hierarchical clustering with that of top-down clustering. The first method is good at identifying small clusters but not large ones; the strengths are reversed for the second method. The hybrid method is built on the new idea of a mutual cluster: a group of points closer to each other than to any other points. Theoretical connections between mutual clusters and bottom-up clustering methods are established, aiding in their interpretation and providing an algorithm for identification of mutual clusters. We illustrate the technique on simulated and real microarray datasets.