Clustering stability and ground truth: numerical experiments - Amorim, M. J.; Cardoso, M. G. M. S. (International Journal of Artificial Intelligence and Knowledge Discovery, 2015)
Stability has been considered an important property for evaluating clustering solutions. Nevertheless, there are no conclusive studies on the relationship between this property and the capacity to recover clusters inherent to data (“ground truth”). This study focuses on this relationship, resorting to experiments on synthetic data generated under diverse scenarios (controlling relevant factors) and experiments on real data sets. Stability is evaluated using a weighted cross-validation procedure. Indices of agreement (corrected for agreement by chance) are used both to assess stability and external validity. The results obtained reveal a new perspective so far not mentioned in the literature. Despite the clear relationship between stability and external validity when a broad range of scenarios is considered, the within-scenarios conclusions deserve our special attention: faced with a specific clustering problem (as we do in practice), there is no significant relationship between clustering stability and the ability to recover data clusters