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PAC Nearest Neighbor Queries: Approximate and Controlled Search in High-Dimensional and Metric Spaces

10 years 5 months ago
PAC Nearest Neighbor Queries: Approximate and Controlled Search in High-Dimensional and Metric Spaces
In high-dimensional and complex metric spaces, determining the nearest neighbor (NN) of a query object ? can be a very expensive task, because of the poor partitioning operated by index structures ? the so-called "curse of dimensionality". This also affects approximately correct (AC) algorithms, which return as result a point whose distance from ? is less than ?????times the distance between ? and its true NN. In this paper we introduce a new approach to approximate similarity search, called PAC-NN queries, where the error bound ? can be exceeded with probability ? and both ? and ? parameters can be tuned at query time to trade the quality of the result for the cost of the search. We describe sequential and index-based PAC-NN algorithms that exploit the distance distribution of the query object in order to determine a stopping condition that respects the error bound. Analysis and experimental evaluation of the sequential algorithm confirm that, for moderately large data sets...
Paolo Ciaccia, Marco Patella
Added 01 Nov 2009
Updated 01 Nov 2009
Type Conference
Year 2000
Where ICDE
Authors Paolo Ciaccia, Marco Patella
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