  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.
