In nearest neighbor searching we are given a set of n data points in real d-dimensional space, R, and the problem is to preprocess these points into a data structure, so that given a query point, the nearest data point to the query point can be reported efficiently. Because data sets can be quite large, we are interested in data structures that use optimal O(dn) storage. Given the limitation of...

With the proliferation of wireless communications and the rapid advances in technologies for tracking the positions of continuously moving objects, algorithms for efficiently answering queries about large numbers of moving objects increasingly are needed. One such query is the reverse nearest neighbor (RNN) query that returns the objects that have a query object as their closest object. While a...

Emerging location-based systems and data analysis frameworks requires efficient management of spatial data for approximate and exact search. Exact similarity search can be done using space partitioning data structures, such as KD-tree, R*-tree, and ball-tree. In this paper, we focus on ball-tree, an efficient search tree that is specific for spatial queries which use euclidean distance. Each no...

Collection of appropriate qualitative and quantitative data is necessary for proper management and planning. Used the suitable inventory methods is necessary and accuracy of sampling methods dependent the inventory net and number of sample point. Nearest neighbor sampling method is a one of distance methods and calculated by three equations (Byth and Riple, 1980; Cotam and Curtis, 1956 and Cota...

This paper describes a novel algorithm for approximate nearest neighbor searching. For solving this problem especially in high dimensional spaces, one of the best-known algorithm is Locality-Sensitive Hashing (LSH). This paper presents a variant of the LSH algorithm that outperforms previously proposed methods when the dataset consists of vectors normalized to unit length, which is often the ca...

In this paper a number of improvements are suggested that can be applied to most k-medoids-based algorithms. These can be divided into two categories conceptual / algorithmic improvements, and implementational improvements. These include the revisiting of the accepted cases for swap comparison and the application of partial distance searching and previous medoid indexing to clustering. We propo...

The kd-tree is a popular and simple data structure for range searching and nearest neighbor searching. Such a tree subdivides space into rectangular cells through the recursive application of some splitting rule. The choice of splitting rule affects the shape of cells and the structure of the resulting tree. It has been shown that an important element in achieving efficient query times for appr...

Similarity search in multimedia databases requires an efficient support of nearest neighbor search on a large set of high-dimensional points. A technique applied for similarity search in multimedia databases is to transform important properties of the multimedia objects into points of a high-dimensional feature space. The feature space is usually indexed using a multidimensional index structure...