A novel approach for k-nearest neighbor (k-NN) searching with Euclidean metric is described. It is well known that many sophisticated algorithms cannot beat the brute-force algorithm when the dimensionality is high. In this study, a probably correct approach, in which the correct set of k-nearest neighbors is obtained in high probability, is proposed for greatly reducing the searching time. We ...

Approximate kNN (k-nearest neighbor) techniques using binary hash functions are among the most commonly used approaches for overcoming the prohibitive cost of performing exact kNN queries. However, the success of these techniques largely depends on their hash functions’ ability to distinguish kNN items; that is, the kNN items retrieved based on data items’ hashcodes, should include as many true...

A Reverse k -Nearest-Neighbor (RkNN) query finds the objects that take the query object as one of their k nearest neighbors. In this paper we propose new solutions for evaluating RkNN queries and its variant bichromatic RkNN queries on 2-dimensional location data. We present an algorithm named INCH that can compute a RkNN query’s search region (from which the query result candidates are drawn)....

In solving pattern recognition problem in the Euclidean space, prototypes representing classes are de ned. On the other hand in the metric space, Nearest Neighbor method and K-Nearest Neighbor method are frequently used without de ning any prototypes. In this paper, we propose a new pattern recognition method for the metric space that can use prototypes which are the centroid of any three patte...

We present a new method for automatic classification of Chinese unknown verbs. The method employs the instance-based categorization using the k-nearest neighbor method for the classification. The accuracy of the classifier is about 70.92%.

Given a point query Q in multi-dimensional space, K-Nearest Neighbor (KNN) queries return the K closest answers in the database with respect to Q. In this scenario, it is possible that a majority of the answers may be very similar to one or more of the other answers, especially when the data has clusters. For a variety of applications, such homogeneous result sets may not add value to the user....

Classification of objects is an important area in a variety of fields and applications. Many different methods are available to make a decision in those cases. The knearest neighbor rule (k-NN) is a well-known nonparametric decision procedure. Classification rules based on the k-NN have already been proposed and applied in diverse substantive areas. The editing k-NN proposed by Wilson would be ...

A visible k nearest neighbor (Vk NN) query retrieves k objects that are visible and nearest to the query object, where “visible”means that there is no obstacle between an object and the query object. Existing studies on the Vk NN query have focused on static data objects. In this paper we investigate how to process the query on moving objects continuously. We queries. We exploit spatial proximi...

Drug repositioning helps identify new indications for marketed drugs and clinical candidates. In this study, we proposed an integrative computational framework to predict novel drug indications for both approved drugs and clinical molecules by integrating chemical, biological and phenotypic data sources. We defined different similarity measures for each of these data sources and utilized a weig...

We adapted the nonparametric evidence-theoretic k-Nearest Neighbor (k-NN) rule,whichwasoriginally designed formultinomial choice data, to rank-ordered choice data. The contribution of thismodel is its ability to extract information fromall theobserved rankings to improve theprediction power for each individual’s primary choice. The evidence-theoretic k-NN rule for heterogeneous rank-ordered dat...