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...

• Type: Conference
• Year: 2000
• Where: ICDE
• Authors: Paolo Ciaccia, Marco Patella
• Keywords: Database, Error Bound, ICDE 2000, Query Object, Sequential Algorithm Confirm