On Feature Selection through Clustering

I
ICDM
2005
Fifth IEEE International Conference on Data Mining (ICDM'05)
Abstract - On Feature Selection through Clustering

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Fifth IEEE International Conference on Data Mining (ICDM'05)

Houston, Texas

November 27-November 30

ISBN: 0-7695-2278-5

	ASCII Text	x
	Richard Butterworth, Gregory Piatetsky-Shapiro, Dan A. Simovici, "On Feature Selection through Clustering," Data Mining, IEEE International Conference on, pp. 581-584, Fifth IEEE International Conference on Data Mining (ICDM'05), 2005.

	BibTex	x
	@article{ 10.1109/ICDM.2005.106, author = {Richard Butterworth and Gregory Piatetsky-Shapiro and Dan A. Simovici}, title = {On Feature Selection through Clustering}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2005}, issn = {1550-4786}, pages = {581-584}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2005.106}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - On Feature Selection through Clustering SN - 1550-4786 SP581 EP584 A1 - Richard Butterworth, A1 - Gregory Piatetsky-Shapiro, A1 - Dan A. Simovici, PY - 2005 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

Richard Butterworth, University of Massachusetts at Boston

Gregory Piatetsky-Shapiro, KDnuggets

Dan A. Simovici, University of Massachusetts at Boston

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2005.106

We study an algorithm for feature selection that clusters attributes using a special metric and then makes use of the dendrogram of the resulting cluster hierarchy to choose the most relevant attributes. The main interest of our technique resides in the improved understanding of the structure of the analyzed data and of the relative importance of the attributes for the selection process.

Citation:

Richard Butterworth, Gregory Piatetsky-Shapiro, Dan A. Simovici, "On Feature Selection through Clustering," icdm, pp.581-584, Fifth IEEE International Conference on Data Mining (ICDM'05), 2005

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