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Density-Based Indexing for Approximate Nearest-Neighbor Queries (1999)

Abstract
We consider the problem of performing nearest-neighbor queries efficiently over large high-dimensional databases. Assuming that a full database scan to determine the nearest neighbor entries is not acceptable, we study the possibility of constructing an index structure over the database. It is well-accepted that traditional database indexing algorithms fail for high-dimensional data (say d?10 or 20 depending on the scheme). Some arguments haveadvocated that nearest-neighbor queries do not even make sense for high-dimensional data since the ratio of maximum and minimum distance goes to 1 as dimensionality increases. We show that these arguments are based on over-restrictive assumptions, and that in the general case it is meaningful and possible to perform such queries. We present an approach for deriving a multidimensional index to support approximate nearestneighbor queries over large databases. Our approach, called DBIN, scales to high-dimensional databases by exploiting sta...

Publication details
Download http://citeseerx.ist.psu.edu/viewdoc/summary?doi=?doi=10.1.1.38.9908
Source ftp://ftp.research.microsoft.com/pub/dtg/fayyad/dbin/tr-98-58-2.pdf
Publisher ACM Press
Contributors CiteSeerX
Repository CiteSeerX - Scientific Literature Digital Library and Search Engine (United States)
Type text
Language English
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