An empirical analysis of techniques for constructing and searching k-dimensional trees

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An empirical analysis of techniques for constructing and searching k-dimensional trees

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International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining table of contents

Boston, Massachusetts, United States

Pages: 26 - 33

Year of Publication: 2000

ISBN:1-58113-233-6

Authors

Douglas A. Talbert	Vanderbilt University, Dept. of Electrical Engineering & Computer Science, Box 1679-B, Nashville, TN
Doug Fisher	Vanderbilt University, Dept. of Electrical Engineering & Computer Science, Box 1679-B, Nashville, TN

Sponsors

SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
AAAI : Am Assoc for Artifical Intelligence
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGMOD: ACM Special Interest Group on Management of Data

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ACM New York, NY, USA

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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	2	Julius S. Bendat , Allan G. Piersol, Random Data: Analysis and Measurement Procedures, John Wiley & Sons, Inc., New York, NY, 1990
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