Cluster-wise assessment of cluster stability
Cluster-wise assessment of cluster stability
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DOI：
10.1016/j.csda.2006.11.025
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发表时间：
2007-09-15
影响因子：
1.8
通讯作者：
Hennig, Christian
中科院分区：
文献类型：
--
作者：
Hennig, Christian
聚类分析的稳定性很大程度上依赖于数据集，特别是取决于聚类的分离程度和同质程度。在同一个集群中，一些集群可能非常稳定，而另一些集群可能非常不稳定。Jaccard系数是一种集合之间的相似性度量，它被用作聚类稳定性的逐聚类度量，该度量是通过相对于引导数据集中最相似的聚类对聚类中的每个单个聚类的Jaccard系数的自举分布来评估的。这可以应用于非常一般的聚类分析方法。文中还研究了几种可供选择的重采样方法，即对数据点进行细分、抖动和用人工噪声点替换部分数据点。通过仿真研究，对不同方法进行了比较。一个数据实例说明了如何使用基于簇的稳定性评估来区分有意义的稳定簇和虚假簇，但也表明，由于某些聚类方法的缺乏灵活性，簇有时只是稳定的。(C)2006爱思唯尔B.V.保留所有权利。
Stability in cluster analysis is strongly dependent on the data set, especially on how well separated and how homogeneous the clusters are. In the same clustering, some clusters may be very stable and others may be extremely unstable. The Jaccard coefficient, a similarity measure between sets, is used as a cluster-wise measure of cluster stability, which is assessed by the bootstrap distribution of the Jaccard coefficient for every single cluster of a clustering compared to the most similar cluster in the bootstrapped data sets. This can be applied to very general cluster analysis methods. Some alternative resampling methods are investigated as well, namely subsetting, jittering the data points and replacing some data points by artificial noise points. The different methods are compared by means of a simulation study. A data example illustrates the use of the cluster-wise stability assessment to distinguish between meaningful stable and spurious clusters, but it is also shown that clusters are sometimes only stable because of the inflexibility of certain clustering methods. (c) 2006 Elsevier B.V. All rights reserved.