This method is also mentioned in the question Evaluation measure of clustering, linked in the comments for this question.
A good resource (with references) for clustering is sklearn's documentation page, Clustering Performance Evaluation.This covers several method, but all but one, the Silhouette Coefficient, assumes ground truth labels are available.Cross-validation has the following applications: You can customize the way that cross-validation works to control the number of cross-sections, the models that are tested, and the accuracy bar for predictions.If you use the cross-validation stored procedures, you can also specify the data set that is used for validating the models.This wealth of choices means that you can easily produce many sets of different results that must then be compared and analyzed.
This section provides information to help you configure cross-validation appropriately.
It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them.
Popular notions of clusters include groups with small distances among the cluster members, dense areas of the data space, intervals or particular statistical distributions.
It uses routines, often called "validation rules" or "check routines", that check for correctness, meaningfulness, and security of data that are input to the system.
The rules may be implemented through the automated facilities of a data dictionary, or by the inclusion of explicit application program validation logic.
In molecular biology protein structure describes the various levels of organization of protein molecules.