Nearest neighbor queries in metric spaces

Given a set S of n sites (points), and a distance measure d, the nearest neighbor searching problem is to build a data structure so that given a query point q, the site nearest to q can be found quickly. This paper gives data structures for this problem when the sites and queries are in a metric space. One data structure, D(S), uses a divide-and-conquer recursion. The other data structure, M(S, Q), is somewhat like a skiplist. Both are simple and implementable. The data structures are analyzed when the metric space obeys a certain sphere-packing bound, and when the sites and query points are random and have distributions with an exchangeability property. This property implies, for example, that query point q is a random element of S boolean OR {q}. Under these conditions, the preprocessing and space bounds for the algorithms are close to linear in n. They depend also on the sphere-packing bound, and on the logarithm of the distance ratio Y(S) of S, the ratio of the distance between the farthest pair of points in S to the distance between the closest pair. The data structure M(S, Q), requires as input data an additional set Q, taken to be representative of the query points. The resource bounds of M(S, Q) have a dependence on the distance ratio of S boolean OR Q. While M(S, Q), can return wrong answers, its failure probability can be bounded, and is decreasing in a parameter K. Here K less than or equal to \Q\/n is chosen when building M(S, Q). The expected query time for M(S, Q) is O(K log n) log Y(S boolean OR Q), and the resource bounds increase linearly in K. The data structure D(S) has expected O (log n)O-(1) query time, for fixed distance ratio. The preprocessing algorithm for M(S, Q) can be used to solve the all nearest neighbor problem for S in O (n(log n)(2)(log Y(S))(2)) expected time.