| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 429111 | Information Processing Letters | 2010 | 4 Pages |
Abstract
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 GEH distance to be used for a much greater variety of scenarios than was possible with the original form.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
