Efficient K-Nearest Neighbor Join Algorithms for High Dimensional Sparse Data
Abstract
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.
Keywords
Cite
@article{arxiv.1011.2807,
title = {Efficient K-Nearest Neighbor Join Algorithms for High Dimensional Sparse Data},
author = {Jijie Wang and Lei Lin and Ting Huang and Jingjing Wang and Zengyou He},
journal= {arXiv preprint arXiv:1011.2807},
year = {2010}
}
Comments
12 pages, This paper has been submitted to PAKDD2011