Efficient reverse k-nearest neighbor search in arbitrary metric spaces

The reverse k-nearest neighbor (RkNN) problem, i.e. finding all objects in a data set the k-nearest neighbors of which include a specified query object, is a generalization of the reverse 1-nearest neighbor problem which has received increasing attention recently. Many industrial and scientific applications call for solutions of the RkNN problem in arbitrary metric spaces where the data objects are not Euclidean and only a metric distance function is given for specifying object similarity. Usually, these applications need a solution for the generalized problem where the value of k is not known in advance and may change from query to query. However, existing approaches, except one, are designed for the specific R1NN problem. In addition -- to the best of our knowledge -- all previously proposed methods, especially the one for generalized RkNN search, are only applicable to Euclidean vector data but not for general metric objects. In this paper, we propose the first approach for efficie...

• Type: Conference
• Year: 2006
• Where: SIGMOD
• Authors: Elke Achtert, Christian Böhm, Peer Kröger, Peter Kunath, Alexey Pryakhin, Matthias Renz
• Keywords: Arbitrary Metric Spaces, Database, K-nearest Neighbor Distance, Reverse K-nearest Neighbor, SIGMOD 2006