On the Generalization of Nearest Neighbor Queries

Nearest neighbor queries on R-trees use a number of pruning techniques to improve the search. We examine three common 1-nearest neighbor pruning strategies and generalize them to k-nearest neighbors. This generalization clears up a number of prior misconceptions. Specifically, we show that the generalization of one pruning technique, referred to as strategy 2, is non-trivial and requires the introduction of a new algorithm we call promise-pruning. In addition, we show that, contrary to other claims, applying this generalized strategy to k-nearest neighbor queries results in a theoretically better search. This discovery is reinforced with empirical results showing the success of promise-pruning on both random and real-world data.