| Abstract |
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The paper addresses the problem of indexing data for the k nearest neighbors (k-nn) search. It presents a tree-based top-down indexing method that uses an iterative k-means algorithm for tree node splitting and combines three different search pruning criteria from BST, GHT and GNAT into one. The experiments show that the presented indexing tree accelerates the k-nn searching up to several thousands times in case of large data sets.
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 Additional Information
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Citation:
Arkadiusz Wojna,
"Center-Based Indexing for Nearest Neighbors Search,"
icdm,
p. 681,
Third IEEE International Conference on Data Mining (ICDM'03),
2003
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