Nearest neighbor search and k-nearest neighbor graph construction are two fundamental issues arise from many disciplines such as information retrieval, data-mining, machine learning and computer vision. Despite continuous efforts have been taken in the last several decades, these two issues remain challenging. They become more and more imminent given the big data emerges in various fields and h...

BACKGROUND While DNA barcoding is an important technology for the authentication of the botanical origins of Chinese medicines, the suitable markers for DNA barcoding of the genus Uncaria have not been reported yet. This study aims to determine suitable markers for DNA barcoding of the genus Uncaria (Gouteng). METHODS Genomic DNA was extracted from the freshly dried leaves of Uncaria plants b...

With the rapid development of Internet technologies, distance education has become a popular educational mode. In this paper, the authors propose an online image automatic annotation distance education system, which could effectively help children learn interrelations between image content and corresponding keywords. Image automatic annotation is a significant problem in image retrieval and ima...

The K-nearest-neighbor decision rule assigns an object of unknown class to the plurality class among the K labeled \training" objects that are closest to it. Closeness is usually de ̄ned in terms of a metric distance on the Euclidean space with the input measurement variables as axes. The metric chosen to de ̄ne this distance can strongly e®ect performance. An optimal choice depends on the proble...

Given a set of n points in d-dimensional Euclidean space, S E d , and a query point q 2 E d , we wish to determine the nearest neighbor of q, that is, the point of S whose Euclidean distance to q is minimum. The goal is to preprocess the point set S, such that queries can be answered as eeciently as possible. We assume that the dimension d is a constant independent of n. Although reasonably goo...

The Nearest Neighbor algorithm is one of the simplest and oldest classification techniques. A given collection of historic data (Training Data) of known classification is stored in memory. Then based on the stored knowledge the classification of an unknown data (Test Data) is predicted by finding the classification of the nearest neighbor. For example, if an instance from the test set is presen...

This paper presents a novel type of queries in spatial databases, called the direction-aware bichromatic reverse k nearest neighbor(DBRkNN ) queries, which extend the bichromatic reverse nearest neighbor queries. Given two disjoint sets, P and S, of spatial objects, and a query object q in S, the DBRkNN query returns a subset P ′ of P such that k nearest neighbors of each object in P ′ include ...

Nearest neighbor is pattern matching method for time series prediction in which most recent values of the time series are compared with previous available values and forecasting is achieved by finding the best match pattern (nearest neighbor). Usually Euclidean distance is used to check the similarity of pattern. In this paper two hybrid criteria of pattern matching are being proposed and evalu...

Spatial pattern of trees can possibly indicate stand history, population dynamics, and species competition. This research was investigated in the Chahartagh forest reserve, Ardal region, chaharmehal and Bakhtiari Province, Central Zagros forest, and southwest Iranian state. In this study 53 hectare of the study area was selected and perfect inventory.  Information includes the position and kind...

The k nearest neighbor classification method is one of the humblest method in conceptually and it is a top method in image mining. In this work, the enhanced k nearest neighbor (EKNN) technique has been implemented to identify the cancer and automatic classification of benign and malignant tissues in the huge amount of lung cancer image datasets. In this proposed system, we have used three stag...