A Framework for Semi-Supervised Learning Based on Subjective and Objective Clustering Criteria

I
ICDM
2005
Fifth IEEE International Conference on Data Mining (ICDM'05)
Abstract - A Framework for Semi-Supervised Learning Based on Subjective and Objective Clustering Criteria

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	Similar Articles Articles by M. Halkidi Articles by D. Gunopulos Articles by N. Kumar Articles by M. Vazirgiannis Articles by C. Domeniconi

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

Houston, Texas

November 27-November 30

ISBN: 0-7695-2278-5

	ASCII Text	x
	M. Halkidi, D. Gunopulos, N. Kumar, M. Vazirgiannis, C. Domeniconi, "A Framework for Semi-Supervised Learning Based on Subjective and Objective Clustering Criteria," Data Mining, IEEE International Conference on, pp. 637-640, Fifth IEEE International Conference on Data Mining (ICDM'05), 2005.

	BibTex	x
	@article{ 10.1109/ICDM.2005.4, author = {M. Halkidi and D. Gunopulos and N. Kumar and M. Vazirgiannis and C. Domeniconi}, title = {A Framework for Semi-Supervised Learning Based on Subjective and Objective Clustering Criteria}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2005}, issn = {1550-4786}, pages = {637-640}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2005.4}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - A Framework for Semi-Supervised Learning Based on Subjective and Objective Clustering Criteria SN - 1550-4786 SP637 EP640 A1 - M. Halkidi, A1 - D. Gunopulos, A1 - N. Kumar, A1 - M. Vazirgiannis, A1 - C. Domeniconi, PY - 2005 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

M. Halkidi, University of California at Riverside and Athens University of Economics and Business

D. Gunopulos, University of California at Riverside

N. Kumar, University of California at Riverside

M. Vazirgiannis, Athens University of Economics and Business

C. Domeniconi, George Mason University

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2005.4

In this paper, we propose a semi-supervised framework for learning a weighted Euclidean subspace, where the best clustering can be achieved. Our approach capitalizes on user-constraints and the quality of intermediate clustering results in terms of its structural properties. It uses the clustering algorithm and the validity measure as parameters.

Citation:

M. Halkidi, D. Gunopulos, N. Kumar, M. Vazirgiannis, C. Domeniconi, "A Framework for Semi-Supervised Learning Based on Subjective and Objective Clustering Criteria," icdm, pp.637-640, Fifth IEEE International Conference on Data Mining (ICDM'05), 2005

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