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Fast and exact fixed-radius neighbor search based on sorting
1Charles University Prague, Prague, Czech Republic.
Peerj. Computer Science
|April 25, 2024
Summary
We introduce SNN, a novel fixed-radius near neighbor search method. SNN offers faster indexing and querying than existing methods, requiring no parameter tuning and delivering exact results.
Area of Science:
- Data Science
- Computer Science
- Machine Learning
Background:
- Fixed-radius near neighbor search is crucial for data analysis.
- Existing approximate methods are computationally intensive during indexing and require parameter tuning.
- Exact brute force and tree-based methods are common but can be slow.
Purpose of the Study:
- To develop a novel, efficient, and exact fixed-radius near neighbor search method.
- To improve upon the performance of brute force and tree-based search algorithms.
- To provide a method that requires no parameter tuning.
Main Methods:
- Proposed a new method called SNN (Sorting-based Near Neighbor search).
- Utilized sorting of data points by their first principal component to prune search space.
- Implemented using Basic Linear Algebra Subprograms (BLAS) for efficiency.
Main Results:
- SNN significantly improves index and query times compared to brute force and tree-based methods.
- SNN guarantees exact search results.
- Demonstrated practical performance improvements both standalone and within DBSCAN clustering.
Conclusions:
- SNN offers a superior alternative for fixed-radius near neighbor search.
- The method is efficient, exact, and user-friendly due to no parameter tuning.
- SNN has potential applications in various data analysis tasks, including clustering.
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