The problem of k-nearest neighbors (kNN) is to find the nearest k neighbors for a query point
from a given data set. Among available methods, the principal axis search tree (PAT) …

The problem of k nearest neighbors (kNN) is to find the nearest k neighbors for a query point
from a given data set. In this paper, a novel fast kNN search method using an orthogonal …

In this paper, a novel algorithm for finding k points that are closest to a query point is
presented. Some inequalities are used to delete impossible data points and reduce distance …

In this paper, we present a fast and versatile algorithm which can rapidly perform a variety of
nearest neighbor searches. Efficiency improvement is achieved by utilizing the distance …

Abstract k-Nearest Neighbors (kNN) search is the problem of finding k points which are the
closest to a given query point. It is used widely in a wide range of tasks and is among the …

We present two new neighbor query algorithms, including range query (RNN) and nearest
neighbor (NN) query, based on revised kd tree by using two techniques. The first technique …

The k-nearest neighbor searching is a classical problem that has been seriously studied,
due to its many important applications. The paper proposes an efficient algorithm to search …

Given n data points in d-dimensional space, k nearest neighbors searching involves
determining k nearest of these data points to a given query point. A depth-first adaptive kNN …

We introduce a fast Nearest Neighbor Search (NNS) algorithm using an Approximate
Cached kd tree (ACk-d tree) structure for low dimensional data sets. The search process of …

Abstract k-nearest neighbor method (k NN) is a very useful and easy-implementing method
for real applications. The query point is estimated by its k nearest neighbors. However, this …