IA Scholar Query: Maximizing bichromatic reverse nearest neighbor for L p -norm in two- and three-dimensional spaces.
https://scholar.archive.org/
Internet Archive Scholar query results feedeninfo@archive.orgWed, 09 Feb 2022 00:00:00 GMTfatcat-scholarhttps://scholar.archive.org/help1440Cluster Nested Loop k-Farthest Neighbor Join Algorithm for Spatial Networks
https://scholar.archive.org/work/q45k5pcdwjg55kluj7634w47vu
This paper considers k-farthest neighbor (kFN) join queries in spatial networks where the distance between two points is the length of the shortest path connecting them. Given a positive integer k, a set of query points Q, and a set of data points P, the kFN join query retrieves the k data points farthest from each query point in Q. There are many real-life applications using kFN join queries, including artificial intelligence, computational geometry, information retrieval, and pattern recognition. However, the solutions based on the Euclidean distance or nearest neighbor search are not suitable for our purpose due to the difference in the problem definition. Therefore, this paper proposes a cluster nested loop join (CNLJ) algorithm, which clusters query points (data points) into query clusters (data clusters) and reduces the number of kFN queries required to perform the kFN join. An empirical study was performed using real-life roadmaps to confirm the superiority and scalability of the CNLJ algorithm compared to the conventional solutions in various conditions.Hyung-Ju Chowork_q45k5pcdwjg55kluj7634w47vuWed, 09 Feb 2022 00:00:00 GMTFinding Relevant Points for Nearest-Neighbor Classification
https://scholar.archive.org/work/hyxjfyn7avhjxcw4okz4mhfgxm
In nearest-neighbor classification problems, a set of d-dimensional training points are given, each with a known classification, and are used to infer unknown classifications of other points by using the same classification as the nearest training point. A training point is relevant if its omission from the training set would change the outcome of some of these inferences. We provide a simple algorithm for thinning a training set down to its subset of relevant points, using as subroutines algorithms for finding the minimum spanning tree of a set of points and for finding the extreme points (convex hull vertices) of a set of points. The time bounds for our algorithm, in any constant dimension d≥ 3, improve on a previous algorithm for the same problem by Clarkson (FOCS 1994).David Eppsteinwork_hyxjfyn7avhjxcw4okz4mhfgxmTue, 12 Oct 2021 00:00:00 GMT