Grid file with approximation: An improved approach for multi-dimensional nearest neighbor search

Cheng Luo, Chih Fang Wang, Wen Chi Hou, Meng Su

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose an approach to improve the grid file approach to the nearest neighbor (NN) search in multi-dimensional data spaces. The original approach is very efficient for low to medium dimensional database applications. However, its performance degrades when dimensionality becomes higher. In order to adapt the approach to higher dimensional databases, we build an approximation file based on the grid file. Then we first search the NN in the approximation file to filter out possible candidates before we actually go to the relative partition to pin down the true NN. By this way, the grid file approach is well adapted to higher dimensional databases. Our simulations show that the improved grid file approach outperforms other approaches in higher dimensional databases.

Original languageEnglish (US)
Title of host publicationProceedings of the 2005 International Conference on Information and Knowledge Engineering, IKE'05
Pages229-235
Number of pages7
Publication statusPublished - Dec 1 2005
Event2005 International Conference on Information and Knowledge Engineering, IKE'05 - Las Vegas, NV, United States
Duration: Jun 20 2005Jun 23 2005

Publication series

NameProceedings of the 2005 International Conference on Information and Knowledge Engineering, IKE'05

Other

Other2005 International Conference on Information and Knowledge Engineering, IKE'05
CountryUnited States
CityLas Vegas, NV
Period6/20/056/23/05

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Information Systems

Cite this

Luo, C., Wang, C. F., Hou, W. C., & Su, M. (2005). Grid file with approximation: An improved approach for multi-dimensional nearest neighbor search. In Proceedings of the 2005 International Conference on Information and Knowledge Engineering, IKE'05 (pp. 229-235). (Proceedings of the 2005 International Conference on Information and Knowledge Engineering, IKE'05).