Lossy Reduction for Very High Dimensional Data

I
ICDE
2002
18th International Conference on Data Engineering (ICDE'02)
Abstract - Lossy Reduction for Very High Dimensional Data

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18th International Conference on Data Engineering (ICDE'02)

San Jose, California

February 26-March 01

ISBN: 0-7695-1531-2

	ASCII Text	x
	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	x
	@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	x
	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

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDE.2002.994783

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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