All Nearest Neighbors Query Including Scores Road Network

Hyo-Kyun Kim, Tae-Sun Chung
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Abstract

This paper introduces an improved ANN (All Nearest Neighbor) algorithm using the SCL (Standard Clustered Loop) algorithm to reduce the consumption of computing resources that can occur when searching for the data object nearest to the query object in the process of executing the algorithm. Additionally, a method to improve ANN algorithm is proposed. When the algorithm is executed, it is a situation in which the user finds a data object adjacent to the user. In this case, our technique applies the criteria set provided by users.
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所有最近的邻居查询,包括分数道路网络
本文介绍了一种改进的ANN (All Nearest Neighbor,全近邻)算法,该算法采用标准集群循环(Standard Clustered Loop, SCL)算法,以减少在算法执行过程中搜索离查询对象最近的数据对象时可能产生的计算资源消耗。此外,还提出了一种改进人工神经网络算法的方法。当执行算法时,是用户找到与用户相邻的数据对象的情况。在这种情况下,我们的技术应用用户提供的标准集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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