A new algorithm for hybrid hierarchical clustering with visualization and the bootstrap
A new algorithm for hybrid hierarchical clustering with visualization and the bootstrap
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DOI：
10.1016/s0378-3758(02)00388-9
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发表时间：
2003-12-01
影响因子：
0.9
通讯作者：
Pollard, KS
中科院分区：
文献类型：
--
作者：
van der Laan, MJ;Pollard, KS
我们提出了一种混合聚类方法，层次有序划分和折叠混合（HOPACH），这是一个层次树的集群。该方法结合了分区和凝聚聚类方法的优势。在每个节点处，集群被划分为两个或更多个较小的集群，其中集群的强制排序。将两个最接近的聚类合并成一个聚类的折叠步骤可以用于校正在划分步骤中产生的错误。我们实现了一个版本的HOPACH，它优化了聚类强度的测量，如平均轮廓，在每个分区和折叠步骤。层次树的一个重要好处是，人们可以在增加的细节级别上查看聚类。我们建议可视化的集群在任何级别的树绘制的距离矩阵对应的排序的集群和集群内的元素的排序。元素的最终有序列表是通过完全运行树来获得的。自举法可用于确定聚类的再现性和随后程序的总体变异性。与目前的算法相比，该方法的力量与模拟和公开的癌症基因表达数据集。(C)2002 Elsevier B.V.保留所有权利。
We propose a hybrid clustering method, hierarchical ordered partitioning and collapsing hybrid (HOPACH), which is a hierarchical tree of clusters. The methodology combines the strengths of both partitioning and agglomerative clustering methods. At each node, a cluster is partitioned into two or more smaller clusters with an enforced ordering of the clusters. Collapsing steps uniting the two closest clusters into one cluster can be used to correct for errors made in the partitioning steps. We implement a version of HOPACH which optimizes a measure of clustering strength, such as average silhouette, at each partitioning and collapsing step. An important benefit of a hierarchical tree is that one can look at clusters at increasing levels of detail. We propose to visualize the clusters at any level of the tree by plotting the distance matrix corresponding with an ordering of the clusters and an ordering of elements within the clusters. A final ordered list of elements is obtained by running down the tree completely. The bootstrap can be used to establish the reproducibility of the clusters and the overall variability of the followed procedure. The power of the methodology compared to current algorithms is illustrated with simulated and publicly available cancer gene expression data sets. (C) 2002 Elsevier B.V. All rights reserved.