  Choosing appropriate hyperparameters for unsupervised clustering algorithms
in an optimal way depending on the problem under study is a long standing
challenge, which we tackle while adapting clustering algorithms for immune
disorder diagnoses. We compare the potential ability of unsupervised clustering
algorithms to detect disease flares and remission periods through analysis of
laboratory data from systemic lupus erythematosus patients records with
different hyperparameter choices. To determine which clustering strategy is the
best one we resort to a Bayesian analysis based on the Plackett-Luce model
applied to rankings. This analysis quantifies the uncertainty in the choice of
clustering methods for a given problem
