Element-centric clustering comparison unifies overlaps and hierarchy
Element-centric clustering comparison unifies overlaps and hierarchy
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DOI：
10.1038/s41598-019-44892-y
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发表时间：
2019-06-12
影响因子：
4.6
通讯作者：
Ahn, Yong-Yeol
中科院分区：
文献类型：
--
作者：
Gates, Alexander J.;Wood, Ian B.;Ahn, Yong-Yeol
聚类是理解复杂数据最通用的方法之一。聚类分析的一个关键方面是定量比较聚类；聚类比较是许多任务的基础，例如聚类评估、共识聚类和跟踪聚类的时间演化。特别是，聚类方法的外部评估需要将未覆盖的聚类与植入的聚类或已知的元数据进行比较。然而，正如我们所证明的，现有的聚类比较措施存在严重偏差，从而削弱了它们的实用性，并且没有任何措施能够同时适应重叠聚类和层次聚类。在这里，我们通过提出一个新的以元素为中心的框架来统一不相交、重叠和层次结构的聚类的比较：元素根据聚类结构引起的关系进行比较，而不是传统的以聚类为中心的哲学。我们证明，与标准聚类相似性度量相比，我们的框架不会受到严重偏差的影响，并且自然地提供了关于聚类差异的独特见解。我们通过在两个应用中揭示对集群组织的新见解来说明我们框架的优势：基于 fMRI 大脑网络的重叠和分层社区结构改进的精神分裂症分类，以及 Facebook 社交网络中各种社会同质因素的解开。聚类的普遍性表明我们的框架对所有科学领域产生了深远的影响。
Clustering is one of the most universal approaches for understanding complex data. A pivotal aspect of clustering analysis is quantitatively comparing clusterings; clustering comparison is the basis for many tasks such as clustering evaluation, consensus clustering, and tracking the temporal evolution of clusters. In particular, the extrinsic evaluation of clustering methods requires comparing the uncovered clusterings to planted clusterings or known metadata. Yet, as we demonstrate, existing clustering comparison measures have critical biases which undermine their usefulness, and no measure accommodates both overlapping and hierarchical clusterings. Here we unify the comparison of disjoint, overlapping, and hierarchically structured clusterings by proposing a new element-centric framework: elements are compared based on the relationships induced by the cluster structure, as opposed to the traditional cluster-centric philosophy. We demonstrate that, in contrast to standard clustering similarity measures, our framework does not suffer from critical biases and naturally provides unique insights into how the clusterings differ. We illustrate the strengths of our framework by revealing new insights into the organization of clusters in two applications: the improved classification of schizophrenia based on the overlapping and hierarchical community structure of fMRI brain networks, and the disentanglement of various social homophily factors in Facebook social networks. The universality of clustering suggests far-reaching impact of our framework throughout all areas of science.