Clustering with missing data: which equivalent for Rubin's rules?. (arXiv:2011.13694v2 [stat.ME] UPDATED)

Multiple imputation (MI) is a popular method for dealing with missing values.
However, the suitable way for applying clustering after MI remains unclear: how
to pool partitions? How to assess the clustering instability when data are
incomplete? By answering both questions, this paper proposed a complete view of
clustering with missing data using MI. The problem of partitions pooling is
here addressed using consensus clustering while, based on the bootstrap theory,
we explain how to assess the instability related to …

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