本文是即将出版的《聚类分析手册》中的一章，Hennig et al.（2015）。关于基本聚类方法和其他一些方法的定义，请参阅《手册》的其他章节。要在没有手册的情况下阅读此版本的文件，需要了解聚类分析方法。本章的目的是为在实践中进行聚类分析时所需的所有决策提供一个框架。概括了聚类的一般态度，它将这些决策与给定应用程序中的聚类目标紧密联系起来。从这个角度来看，本章然后讨论了数据处理的方面，如选择的对象表示聚类，相异性设计，变量的转换和标准化。在聚类方法的选择上，探讨了不同的聚类方法如何对应不同的聚类目标。然后，概述了比较不同聚类方法的基准研究，以及概述了用公理来描述聚类所需条件的理论方法。最后，聚类验证的方面，即，在给定的数据集中，聚类质量的评估，包括寻找合适的数量的集群，测试同质性，内部和外部的集群验证，评估聚类的稳定性和数据可视化进行了讨论。
英文摘要：
This paper is a chapter in the forthcoming Handbook of Cluster Analysis, Hennig et al. (2015). For definitions of basic clustering methods and some further methodology, other chapters of the Handbook are referred to. To read this version of the paper without the Handbook, some knowledge of cluster analysis methodology is required.
The aim of this chapter is to provide a framework for all the decisions that are required when carrying out a cluster analysis in practice. A general attitude to clustering is outlined, which connects these decisions closely to the clustering aims in a given application. From this point of view, the chapter then discusses aspects of data processing such as the choice of the representation of the objects to be clustered, dissimilarity design, transformation and standardization of variables. Regarding the choice of the clustering method, it is explored how different methods correspond to different clustering aims. Then an overview of benchmarking studies comparing different clustering methods is given, as well as an out- line of theoretical approaches to characterize desiderata for clustering by axioms. Finally, aspects of cluster validation, i.e., the assessment of the quality of a clustering in a given dataset, are discussed, including finding an appropriate number of clusters, testing homogeneity, internal and external cluster validation, assessing clustering stability and data visualization.
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