When assessing the qualities of a resulting clustering for most measures a goldstandard corresponding to the dataset is needed. The comparison of the clustering and goldstandard is then integrated into the calculation of the clustering quality measure (see 4.6).
Goldstandards for a dataset are in principle optional. Some operations can be also performed without a goldstandard and also some clustering quality measures do not require a goldstandard (for example Silhouette Value).
Nevertheless, to be able to perform all operations on the dataset, a goldstandard is required. To add a goldstandard to the framework, you have to
- insert the goldstandard file into the repository of the backend
- specify the goldstandard in a configuration file (called goldstandard configuration)
The goldstandard configuration contains the name and path to the goldstandard file. Since the framework does only support one goldstandard format, this does not need to be provided. An exact description of how goldstandard configurations look like and which options and settings need to be specified can be found in 4.9.5.