Finding aggregate nearest neighbor efficiently without indexing

Aggregate Nearest Neighbor Queries are much more complex than Nearest Neighbor queries, and pruning strategies are always utilized in ANN queries. Most of the pruning methods are based on the data index mechanisms, such as R-tree. But for the well-known curse of dimensionality, ANN search could be meaningless in high dimensional spaces. In this paper, we propose two non-index pruning strategies in ANN queries on metric space. Our methods utilize the r-NN query and projecting law, analyze the distributing of query points, find out the search region in data space, and get the result efficiently.