An Approach for Searching the Top K Nearest Areas Based on R-tree

The K-nearest neighbor (KNN) fundamental MBR searching is a widely used technique in navigation software. It helps locate regions, such as nearby buildings, that cannot be treated as individual entities. When searching for the top k nearest fundamental MBRs or KNN points in an unevenly distributed s...

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Bibliographic Details
Published in2023 International Conference on Networks, Communications and Intelligent Computing (NCIC) pp. 267 - 272
Main Authors Lin, Wenhao, Lin, Yifeng, Yang, Yuer
Format Conference Proceeding
LanguageEnglish
Published IEEE 17.11.2023
Subjects
Approximation algorithms
Heuristic algorithms
K-nearest Neighbors (KNN)
Linear Scanning
Minimum Bounding Rectangle (MBR)
Navigation
R-tree
Search problems
Software
Software algorithms
Technological innovation
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Summary:The K-nearest neighbor (KNN) fundamental MBR searching is a widely used technique in navigation software. It helps locate regions, such as nearby buildings, that cannot be treated as individual entities. When searching for the top k nearest fundamental MBRs or KNN points in an unevenly distributed space, algorithms based on the R-tree prove to be the most effective ones. R-tree allows for efficient indexing and searching of multidimensional space, making it a powerful tool for spatial querying and top k nearest neighbor searching. In this paper, we compare KNN based on R-tree with linear scanning in 2D graph-based search algorithms. Experimental results demonstrate that our proposed searching algorithms based on R-tree outperform linear scanning by being approximately 95.357% faster in range querying and approximately 97.826% faster in finding the top k nearest fundamental MBRs. Furthermore, the searching algorithms based on the R-tree can be extended to find the top k nearest fundamental MBRs of q in high-dimensional spaces.
DOI:10.1109/NCIC61838.2023.00051