In this paper, a comparison between Cosine Similarity and k-Nearest Neighbors algorithm in Latent Semantic Analysis method to score Arabic essays automatically is presented. It also improves Latent Semantic Analysis by processing the entered text, unifying the form of letters, deleting the formatting, replacing synonyms, stemming and deleting "Stop Words". The results showed that the use of Cos...

Nearest Neighbor (NN) searching is a challenging problem in data management and has been widely studied in data mining, pattern recognition and computational geometry. The goal of NN searching is efficiently reporting the nearest data to a given object as a query. In most of the studies both the data and query are assumed to be precise, however, due to the real applications of NN searching, suc...

In the era of big data, k-means clustering has been widely adopted as a basic processing tool in various contexts. However, its computational cost could be prohibitively high as the data size and the cluster number are large. It is well known that the processing bottleneck of k-means lies in the operation of seeking closest centroid in each iteration. In this paper, a novel solution towards the...

The problem of k-nearest neighbors (kNN) search is to find nearest k neighbors from a given data set for a query point. To speed up the finding process of nearest k neighbors, many fast kNN search algorithms were proposed. The performance of fast kNN search algorithms is highly influenced by the number of dimensions, number of data points, and data distribution of a data set. In the extreme cas...

In this paper, a novel algorithm for enhancing the performance of classification is proposed. This new method provides rich information for clustering and outlier detection. We call it Natural Nearest Neighbor with Quality (3N-Q). Comparing to K-nearest neighbor and E-nearest neighbor, 3N-Q employs a completely different concept to find the nearest neighbors passively, which can adaptively and ...

Motivated by promising experimental results, this paper investigates the theoretical properties of a recently proposed nonparametric estimator, called the Mutual Nearest Neighbors rule, which estimates the regression function m(x) = E[Y |X = x] as follows: first identify the k nearest neighbors of x in the sample Dn, then keep only those for which x is itself one of the k nearest neighbors, and...

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 ...

One of the difficulties that arises when using the K-nearest neighbor rule is that each of the labeled training samples is given equal importance in deciding the class of the query pattern to be classified, regardless of their typicality. In this paper, the theory of belief functions is introduced into the K-nearest neighbor rule to develop an evidential editing version of this algorithm. An ev...

Mahalanobis distance metric takes feature weights and correlation into account in the distance computation, which can improve the performance of many similarity/dissimilarity based methods, such as kNN. Most existing distance metric learning methods obtain metric based on the raw features and side information but neglect the reliability of them. Noises or disturbances on instances will make cha...

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...