  The stream-based Max-Min diversification problem concerns the task of
selecting a limited number of diverse instances from a data stream. The nature
of the problem demands immediate and irrevocable decisions. The set-wise
diversity to be maximized is the minimum distance among any pair of the
selected instances. Standard algorithmic approaches for sequential selection
disregard the possibility of selection failures, which is the situation where
the last instances of the stream are picked by default to prevent having an
incomplete selection. This defect can be catastrophic for the Max-Min
diversification objective. In this paper we present the Failure Rate
Minimization (FRM) algorithm that allows the selection of a set of disparate
instances while reducing significantly the probability of having failures. This
is achieved by means of both analytical and empirical techniques. FRM is put in
comparison with relevant algorithms from the literature through simulations on
real datasets, where we demonstrate its efficiency and low time complexity.
