On k-Nearest Neighbor Searching in Non-Ordered Discrete Data Spaces

A k-nearest neighbor (k-NN) query retrieves k objects from a database that are considered to be the closest to a given query point. Numerous techniques have been proposed in the past for supporting efficient kNN searches in continuous data spaces. No such work has been reported in the literature for k-NN searches in a non-ordered discrete data space (NDDS). Performing k-NN searches in an NDDS raises new challenges. The Hamming distance is usually used to measure the distance between two vectors (objects) in an NDDS. Due to the coarse granularity of the Hamming distance, a k-NN query in an NDDS may lead to a large set of candidate solutions, creating a high degree of nondeterminism for the query result. We propose a new distance measure, called Granularity-Enhanced Hamming (GEH) distance, that effectively reduces the number of candidate solutions for a query. We have also considered using multidimensional database indexing for implementing k-NN searches in NDDSs. Our experiments on syn...

• Type: Conference
• Year: 2007
• Where: ICDE
• Authors: Dashiell Kolbe, Qiang Zhu, Sakti Pramanik
• Keywords: Database, Efficient KNN Searches, Granularity-Enhanced Hamming, ICDE 2007, Query Point