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...

Spectral clustering is a well-known graph-theoretic algorithm. Although spectral has several desirable advantages (such as the capability of discovering non-convex clusters and applicability to any data type), it often leads incorrect results because high sensitivity noise points. In this study, we propose robust algorithm known KNN-SC that can discover exact by decreasing influence To achieve ...