基于排序的快速和精确的固定半径邻居搜索
1Charles University Prague, Prague, Czech Republic.
PeerJ. Computer science
|April 25, 2024
概括
我们介绍SNN,一种新的固定半径近邻搜索方法. SNN提供比现有方法更快的索引和查询,不需要参数调整,并提供准确的结果.
科学领域:
- 数据科学数据科学数据科学
- 计算机科学 计算机科学
- 机器学习 机器学习
背景情况:
- 固定半径近邻搜索对于数据分析至关重要.
- 现有的近似方法在索引时是计算密集的,需要参数调整.
- 精确的武力和基于树的方法是常见的,但可能很慢.
研究的目的:
- 开发一种新,高效,精确的固定半径近邻搜索方法.
- 为了提高粗暴武力和基于树的搜索算法的性能.
- 提供一种不需要参数调整的方法.
主要方法:
- 提出了一个名为SNN (基于排序的近邻搜索) 的新方法.
- 使用数据点按其第一个主要组件进行排序,以削减搜索空间.
- 为了提高效率,使用基本线性代数子程序 (BLAS) 实现.
主要成果:
- 与武力和基于树的方法相比,SNN显著提高了索引和查询时间.
- SNN保证准确的搜索结果.
- 证明了独立和DBSCAN集群中的实际性能改进.
结论:
- SNN为固定半径近邻搜索提供了一种优越的替代方案.
- 该方法是高效的,准确的,并且由于没有参数调整而易于使用.
- 在各种数据分析任务中,SNN具有潜在的应用,包括聚类.
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