The KNN algorithm is one of the most popular data mining algorithms. It has been widely
and successfully applied to data analysis applications across a variety of research topics in …

In this paper, we propose a coarse to fine K nearest neighbor (KNN) classifier (CFKNNC).
CFKNNC differs from the conventional KNN classifier (CKNNC) as follows: CFKNNC first …

The existing noise detection methods required the classifiers or distance measurements or
data overall distribution, andcurse of dimensionality'and other restrictions made them …

k nearest neighbor (kNN) is an effective and powerful lazy learning algorithm,
notwithstanding its easy-to-implement. However, its performance heavily relies on the …

This paper presents a novel approach to perform fast approximate nearest neighbors search
in high dimensional data, using a nearest neighbor graph created over large collections …

The massive increase of spam is posing a very serious threat to email which has become an
important means of communication. Not only does it annoy users, but it also consumes much …

The conventional nearest neighbor classifier (NNC) directly exploits the distances between
the test sample and training samples to perform classification. NNC independently evaluates …

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 …

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 …

Fast approximate nearest neighbor search has been well studied for real-valued vectors,
however, the methods for binary descriptors are less developed. The paper addresses this …