With m processors available, the k-nearest neighbor classifier can be straightforwardly parallelized with a linear speed increase of factor m. In this paper we introduce two methods that in principle are able to achieve this aim. The first method splits the test set in m parts, while the other distributes the training set over m sub-classifiers, and merges their m nearest neighbor sets with eac...

We study a new type of queries called the k-nearest neighbor temporal aggregate (kNNTA) query. Given a query point and a time interval, it returns the top-k locations that have the smallest weighted sums of (i) the spatial distance to the query point and (ii) a temporal aggregate on a certain attribute over the time interval. For example, find a nearby club that has the largest number of people...

The k-nearest neighbor (k-NN) is one of the most popular algorithms used for classification in various fields of pattern recognition & data mining problems. In k-nearest neighbor classification, the result of a new instance query is classified based on the majority of k-nearest neighbors. Recently researchers have begun paying attention to combining a set of individual k-NN classifiers, each us...

Proximity searching is the problem of retrieving, from a given database, those objects closest to a query. To avoid exhaustive searching, data structures called indexes are built on the database prior to serving queries. The curse of dimensionality is a well-known problem for indexes: in spaces with sufficiently concentrated distance histograms, no index outperforms an exhaustive scan of the da...

This paper provides the first solution to the kinetic reverse k-nearest neighbor (RkNN) problem in R, which is defined as follows: Given a set P of n moving points in arbitrary but fixed dimension d, an integer k, and a query point q / ∈ P at any time t, report all the points p ∈ P for which q is one of the k-nearest neighbors of p.

In some regression problems, it may be more reasonable to predict intervals rather than precise values. We are interested in finding intervals which simultaneously for all input instances x ∈ X contain a β proportion of the response values. We name this problem simultaneous interval regression. This is similar to simultaneous tolerance intervals for regression with a high confidence level γ ≈ 1...

—The SPY-TEC (Spherical Pyramid-Technique) was proposed as a new indexing method for high-dimensional data spaces using a special partitioning strategy that divides a d-dimensional data space into 2d spherical pyramids. In the SPY-TEC, an efficient algorithm for processing hyperspherical range queries was introduced with a special partitioning strategy. However, the technique for processing k-n...

Motivation: With the emerging success of protein secondary structure prediction through the applications of various statistical and machine learning techniques, similar techniques have been applied to protein β-turn prediction. In this study, we perform protein β-turn prediction using a k -nearest neighbor method, which is combined with a filter that uses predicted protein secondary structure i...

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...

A description is given of how to use an estimate of the maximum possible distance at which a nearest neighbor can be found to prune the search process in a depth-first branch and bound k-nearest neighbor finding algorithm.