验证数据上聚类分析结果的验证：一个系统框架
Validation of cluster analysis results on validation data: A systematic framework
摘要：
聚类分析是一种用于类发现的数据分析技术，在许多应用领域都很流行。为了判断聚类结果的质量，文献中提出了不同的聚类验证程序。虽然有大量的工作经典的验证技术，如内部和外部验证，很少有人注意到验证和复制使用验证数据集的聚类结果。这样的数据集可以是原始数据集的一部分，在分析开始之前分离，或者它可以是独立收集的数据集。我们提出了一个系统的结构化框架，用于验证聚类结果的验证数据，包括大多数现有的验证方法。特别是，我们回顾经典的验证技术，如内部和外部验证，稳定性分析，假设检验，和视觉验证，并显示如何可以解释我们的框架。我们精确地定义和形式化验证数据集上的聚类结果的不同类型的验证，并解释如何在实践中实现每种类型。此外，我们给出的例子，如何聚类研究的应用文献，使用验证数据集可以被归类到框架。
英文摘要：
Cluster analysis refers to a wide range of data analytic techniques for class discovery and is popular in many application fields. To judge the quality of a clustering result, different cluster validation procedures have been proposed in the literature. While there is extensive work on classical validation techniques, such as internal and external validation, less attention has been given to validating and replicating a clustering result using a validation dataset. Such a dataset may be part of the original dataset, which is separated before analysis begins, or it could be an independently collected dataset. We present a systematic structured framework for validating clustering results on validation data that includes most existing validation approaches. In particular, we review classical validation techniques such as internal and external validation, stability analysis, hypothesis testing, and visual validation, and show how they can be interpreted in terms of our framework. We precisely define and formalise different types of validation of clustering results on a validation dataset and explain how each type can be implemented in practice. Furthermore, we give examples of how clustering studies from the applied literature that used a validation dataset can be classified into the framework.
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