Reducing non-determinism of k-NN searching in non-ordered discrete data spaces

Authors:
Dashiell Kolbe;Qiang Zhu;Sakti Pramanik
Affiliations:
Michigan State University, East Lansing, MI, United States;University of Michigan - Dearborn, Dearborn, MI, United States;Michigan State University, East Lansing, MI, United States
Venue:
Information Processing Letters
Year:
2010

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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.