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Lossy Reduction for Very High Dimensional Data
San Jose, California February 26-March 01
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDE.2002.99478318th International Conference on Data ...
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Christopher Jermaine, Edward Omiecinski, "Lossy Reduction for Very High Dimensional Data," Data Engineering, International Conference on, pp. 0663, 18th International Conference on Data Engineering (ICDE'02), 2002.
 
 
BibTex
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@article{ 10.1109/ICDE.2002.994783,
author = {Christopher Jermaine and Edward Omiecinski},
title = {Lossy Reduction for Very High Dimensional Data},
journal ={Data Engineering, International Conference on},
volume = {0},
year = {2002},
isbn = {0-7695-1531-2},
pages = {0663},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICDE.2002.994783},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Data Engineering, International Conference on
TI - Lossy Reduction for Very High Dimensional Data
SN - 0-7695-1531-2
SP
EP
A1 - Christopher Jermaine,
A1 - Edward Omiecinski,
PY - 2002
VL - 0
JA - Data Engineering, International Conference on
ER -
 
Christopher Jermaine, Georgia Institute of Technology
Edward Omiecinski, Georgia Institute of Technology
We consider the use of data reduction techniques for the problem of approximate query answering. We focus on applications for which accurate answers to selective queries are required, and for which the data are very high dimensional (having hundreds of attributes). We present a new data reduction method for this type of application, called the RS Kernel. We demonstrate the effectiveness of this method for answering difficult, highly selective queries over high dimensional data using several real datasets.
Citation:
Christopher Jermaine, Edward Omiecinski, "Lossy Reduction for Very High Dimensional Data," icde, pp.0663, 18th International Conference on Data Engineering (ICDE'02), 2002
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