Collection of appropriate qualitative and quantitative data is necessary for proper management and planning. Used the suitable inventory methods is necessary and accuracy of sampling methods dependent the inventory net and number of sample point. Nearest neighbor sampling method is a one of distance methods and calculated by three equations (Byth and Riple, 1980; Cotam and Curtis, 1956 and Cota...

This paper presents an algorithm, called the winnerupdate algorithm, for accelerating the nearest neighbor search. By constructing a hierarchical structure for each feature point in the lp metric space, this algorithm can save a large amount of computation at the expense of moderate preprocessing and twice the memory storage. Given a query point, the cost for computing the distances from this p...

Given a set of n points in d-dimensional Euclidean space, S E d , and a query point q 2 E d , we wish to determine the nearest neighbor of q, that is, the point of S whose Euclidean distance to q is minimum. The goal is to preprocess the point set S, such that queries can be answered as eeciently as possible. We assume that the dimension d is a constant independent of n. Although reasonably goo...

The simple k nearest neighbor method is often very competitive, especially in classiication methods. When the number of predictors is large, the nearest neighbors are likely to be quite distant from the target point. Furthermore they tend to all be on one side of the target point. These are consequences of high dimensional geometry. This paper introduces a modiication of nearest neighbors that ...

This paper studies nearest neighbor classification in a model where unlabeled data points arrive in a stream, and the learner decides, for each one, whether to ask for its label. Are there generic ways to augment or modify any selective sampling strategy so as to ensure the consistency of the resulting nearest neighbor classifier?

For an arbitrary point of a homogeneous Poisson point process in a d-dimen-siona! Euclidean space, consider the number of Poisson points that have that given point as their r-th nearest neighbor (r = 1,2,...). It is shown that as d tends to infinity, these nearest neighbor counts (r = 1,2,...) are tid asymptotically Poisson with mean 1. The proof relies on Renyi's characterization of Poisson pr...

Any connected plane nearest neighbor graph has diameter Ω(n). This bound generalizes to Ω(n) in any dimension d. For any set of n points in the plane, we define the nearest neighbor graph by selecting a unique nearest neighbor for each point, and adding an edge between each point and its neighbor. This is a directed graph with outdegree one; thus it is a pseudo-forest. Each component of the pse...

The nearest neighbor search (NNS) problem is the following: Given a set of n pointsP = fp1; : : : ; png in some metric spaceX, preprocess P so as to efficiently answer queries which require finding a point in P closest to a query point q 2 X. The approximate nearest neighbor search (c-NNS) is a relaxation of NNS which allows to return any point within c times the distance to the nearest neighbo...

The nearest neighbor search (NNS) problem is the following: Given a set of n points P = fp 1 ; : : : ; p n g in some metric space X , preprocess P so as to eeciently answer queries which require nding a point in P closest to a query point q 2 X. The approximate nearest neighbor search (c-NNS) is a relaxation of NNS which allows to return any point within c times the distance to the nearest neig...