Nearest neighbor search in general metric spaces using a tree data structure with a simple heuristic

We present a new algorithm for nearest neighbor search in general metric spaces. The algorithm organizes the database into recursively partitioned Voronoi regions and represents these partitions in a tree. The separations between the Voronoi regions as well as the radius of each region are used with triangular inequality to derive the minimum possible distance between any point in a region and the query and to discard the region from further search if a smaller distance has already been found. The algorithm also orders the search sequence of the tree branches using the estimate of the minimum possible distance. This simple heuristic proves to considerably enhance the pruning of the search tree. The efficiency of the algorithm is demonstrated on several artificial data sets and real problems in computational chemistry.