An Attempt to Find Neighbors

Yong Shi, Ryan Rosenblum
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Abstract

In this paper, we present our continuous research on similarity search problems. Previously we proposed PanKNN[18]which is a novel technique that explores the meaning of K nearest neighbors from a new perspective, redefines the distances between data points and a given query point Q, and efficiently and effectively selects data points which are closest to Q. It can be applied in various data mining fields. In this paper, we present our approach to solving the similarity search problem in the presence of obstacles. We apply the concept of obstacle points and process the similarity search problems in a different way. This approach can assist to improve the performance of existing data analysis approaches.
试图寻找邻居
本文介绍了我们对相似搜索问题的持续研究。在此之前,我们提出了一种新颖的技术PanKNN[18],它从新的角度探索K个最近邻的含义,重新定义数据点与给定查询点Q之间的距离,并高效地选择最接近Q的数据点,可以应用于各种数据挖掘领域。在本文中,我们提出了一种解决存在障碍的相似搜索问题的方法。我们运用障碍点的概念,以不同的方式处理相似搜索问题。这种方法有助于提高现有数据分析方法的性能。
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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