Reducing non-determinism of k-NN searching in non-ordered discrete data spaces
We propose a generalized version of the Granularity-Enhanced Hamming (GEH) distance for use in k-NN queries in non-ordered discrete data spaces (NDDS). The use of the GEH distance metric improves search semantics by reducing the degree of non-determinism of k-NN queries in NDDSs. The generalized form presented here enables the GEN distance to be used for a much greater variety of scenarios than was possible with the original form. (C) 2010 Elsevier B.V. All rights reserved.
non-ordered discrete data space algorithms databases information retrieval k-nearest neighbor search