## Nearest-neighbor searching and metric space dimensions (2006)

Venue: | In Nearest-Neighbor Methods for Learning and Vision: Theory and Practice |

Citations: | 87 - 0 self |

### BibTeX

@INPROCEEDINGS{Clarkson06nearest-neighborsearching,

author = {Kenneth L. Clarkson},

title = {Nearest-neighbor searching and metric space dimensions},

booktitle = {In Nearest-Neighbor Methods for Learning and Vision: Theory and Practice},

year = {2006},

publisher = {MIT Press}

}

### Years of Citing Articles

### OpenURL

### Abstract

Given a set S of n sites (points), and a distance measure d, the nearest neighbor searching problem is to build a data structure so that given a query point q, the site nearest to q can be found quickly. This paper gives a data structure for this problem; the data structure is built using the distance function as a “black box”. The structure is able to speed up nearest neighbor searching in a variety of settings, for example: points in low-dimensional or structured Euclidean space, strings under Hamming and edit distance, and bit vector data from an OCR application. The data structures are observed to need linear space, with a modest constant factor. The preprocessing time needed per site is observed to match the query time. The data structure can be viewed as an application of a “kd-tree ” approach in the metric space setting, using Voronoi regions of a subset in place of axis-aligned boxes. 1