Validation of cluster analysis results on validation data: A systematic framework

Cluster analysis refers to a wide range of data analytic techniques for class discovery and is popular in many application fields. To assess 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 review of the existing literature about this topic. For this purpose, we outline a formal framework that covers most existing approaches for validating clustering results on validation data. In particular, we review classical validation techniques such as internal and external validation, stability analysis, and visual validation, and show how they can be interpreted in terms of our framework. We define and formalize different types of validation of clustering results on a validation dataset, and give examples of how clustering studies from the applied literature that used a validation dataset can be seen as instances of our framework. This article is categorized under: Technologies > Structure Discovery and Clustering Algorithmic Development > Statistics Technologies > Machine Learning

Automated summary

Cluster analysis is a popular data analytic technique for class discovery, with different methods for assessing the quality of clustering results. While there is extensive work on traditional validation techniques, more attention needs to be given to validating clustering results using a separate validation dataset. This article provides a systematic review of existing literature on this topic and outlines a formal framework for validating clustering results on validation data.