A mobile agent in a network wants to visit every node of an n-node network, using a small number of steps. We investigate the performance of the following \nearest neighbor" heuristic: always go to the nearest unvisited node. If the network graph never changes, then from (Rosenkrantz, Stearns and Lewis, 1977) and (Hurkens and Woeginger, 2004) it follows that (n logn) steps are necessary and su ...

NEAREST NEIGHBOR IMPUTATION Jiahua Chen1 University of Waterloo Jun Shao2 University of Wisconsin-Madison Abstract Nearest neighbor imputation is one of the hot deck methods used to compensate for nonresponse in sample surveys. Although it has a long history of application, theoretical properties of the nearest neighbor imputation method are unknown prior to the current paper. We show that unde...

The “nearest neighbor” relation, or more generally the “k nearest neighbors” relation, defined for a set of points in a metric space, has found many uses in computational geometry and clustering analysis, yet surprisingly little is known about some of its basic properties. In this paper, we consider some natural questions that are motivated by geometric embedding problems. We derive bounds on t...

The nearest-neighbor method is perhaps the simplest of all algorithms for predicting the class of a test example. The training phase is trivial: simply store every training example, with its label. To make a prediction for a test example, first compute its distance to every training example. Then, keep the k closest training examples, where k ≥ 1 is a fixed integer. Look for the label that is m...

Benjamini, Pemantle, and Peres constructed nearest neighbor processes which have predictability profiles that decay faster than that of the simple random walk. Häggström and Mossel found processes with even faster decaying predictability profiles. We prove that rate of decay achieved by Häggström and Mossel is optimal.

We introduce the visible k nearest neighbor (VkNN) query, which finds the k nearest objects that are visible to a query point. We also propose an algorithm to efficiently process the VkNN query. We compute the visible neighbors incrementally as we enlarge the search space. Our algorithm dramatically reduces the search cost compared to existing methods that require the computation of the visibil...

Several researchers proposed using non-Euclidean metrics on point sets in Euclidean space for clustering noisy data. Almost always, a distance function is desired that recognizes the closeness of the points in the same cluster, even if the Euclidean cluster diameter is large. Therefore, it is preferred to assign smaller costs to the paths that stay close to the input points. In this paper, we c...

This paper presents the nearest neighbor value (NNV) algorithm for high resolution (H.R.) image interpolation. The difference between the proposed algorithm and conventional nearest neighbor algorithm is that the concept applied, to estimate the missing pixel value, is guided by the nearest value rather than the distance. In other words, the proposed concept selects one pixel, among four direct...