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  A model for k-Nearest Neighbor query processing cost in multidimensional data space (1999) [1 citations — 0 self]

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by Ju-hong Lee, Guang-ho Cha, Chin-wan Chung
Information Processing Letters
http://islab.kaist.ac.kr/~jhlee/ps/knn-ipl-final.pdf
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Abstract:

A cost model for the performance of the k-nearest neighbor query in multidimensional data space is presented. Two concepts, the regional average volume and the density function, are introduced to predict the performance for uniform and non-uniform data distributions. The experiment shows that the prediction based on this model is accurate within an acceptable range of the error in low and mid dimensions.

Citations

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71 Towards an analysis of range query performance in spatial data structures – Pagel, Six, et al. - 1993
2 Analysis of Nearest Neighbor Query Performance – Cha, Park, et al. - 1997
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