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The stream-based Max-Min diversification problem concerns the task ofselecting a limited number of diverse instances from a data stream. The natureof the problem demands immediate and irrevocable decisions. The set-wisediversity to be maximized is the minimum distance among any pair of theselected instances. Standard algorithmic approaches for sequential selectiondisregard the possibility of selection failures, which is the situation wherethe last instances of the stream are picked by default to prevent having anincomplete selection. This defect can be catastrophic for the Max-Mindiversification objective. In this paper we present the Failure RateMinimization (FRM) algorithm that allows the selection of a set of disparateinstances while reducing significantly the probability of having failures. Thisis achieved by means of both analytical and empirical techniques. FRM is put incomparison with relevant algorithms from the literature through simulations onreal datasets, where we demonstrate its efficiency and low time complexity.

Efficient stream-based Max-Min diversification with minimal failure rate