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 …

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) …

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 …

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 …

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 …

The k-nearest neighbors (k-NN) algorithm is a widely used machine learning method that
finds nearest neighbors of a test object in a feature space. We present a new exact k-NN …

The k-nearest neighbor (KNN) algorithm has been widely used in pattern recognition,
regression, outlier detection and other data mining areas. However, it suffers from the large …

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 …

The k-nearest neighbors (KNN) algorithm remains a useful and widely applied approach. In
the recent years, we have seen many advances in KNN methods, but few research works …

Abstract k-nearest neighbors (kNN) classifiers are commonly used in various applications
due to their relative simplicity and the absence of necessary training. However, the time …