高维稀疏数据的高效K-近邻连接算法
Efficient K-Nearest Neighbor Join Algorithms for High Dimensional Sparse Data
摘要：
在许多数据采掘算法中，K-最近邻（KNN）连接是一个代价昂贵但非常重要的操作。最近的几个应用程序需要执行高维稀疏数据的KNN连接。不幸的是，所有现有的KNN连接算法都是针对低维数据设计的。为了弥补这一空白，我们研究了高维稀疏数据的KNN连接问题。在本文中，我们提出了三个KNN连接算法：一个暴力（BF）算法，一个基于倒排索引（IIB）算法和一个改进的基于倒排索引（IIIB）算法。在合成数据集和真实数据集上进行了大量的实验，以证明我们的算法对高维稀疏数据的有效性。
英文摘要：
The K-Nearest Neighbor (KNN) join is an expensive but important operation in many data mining algorithms. Several recent applications need to perform KNN join for high dimensional sparse data. Unfortunately, all existing KNN join algorithms are designed for low dimensional data. To fulfill this void, we investigate the KNN join problem for high dimensional sparse data. In this paper, we propose three KNN join algorithms: a brute force (BF) algorithm, an inverted index-based(IIB) algorithm and an improved inverted index-based(IIIB) algorithm. Extensive experiments on both synthetic and real-world datasets were conducted to demonstrate the effectiveness of our algorithms for high dimensional sparse data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
