  The Mallows model occupies a central role in parametric modelling of ranking
data to learn preferences of a population of judges. Despite the wide range of
metrics for rankings that can be considered in the model specification, the
choice is typically limited to the Kendall, Cayley or Hamming distances, due to
the closed-form expression of the related model normalizing constant. This work
instead focuses on the Mallows model with Spearman distance. An efficient and
accurate EM algorithm for estimating finite mixtures of Mallows models with
Spearman distance is developed, by relying on a twofold data augmentation
strategy aimed at i) enlarging the applicability of Mallows models to samples
drawn from heterogeneous populations; ii) dealing with partial rankings
affected by diverse forms of censoring. Additionally, a novel approximation of
the model normalizing constant is introduced to support the challenging
model-based clustering of rankings with a large number of items. The
inferential ability of the EM scheme and the effectiveness of the approximation
are assessed by extensive simulation studies. Finally, we show that the
application to three real-world datasets endorses our proposals also in the
comparison with competing mixtures of ranking models.
