  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.
