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2013 IEEE 13th International Conference on Data Mining (2005)
Houston, Texas
Nov. 27, 2005 to Nov. 30, 2005
ISSN: 1550-4786
ISBN: 0-7695-2278-5
pp: 637-640
D. Gunopulos , University of California at Riverside
M. Vazirgiannis , Athens University of Economics and Business
M. Halkidi , University of California at Riverside and Athens University of Economics and Business
N. Kumar , University of California at Riverside
C. Domeniconi , George Mason University
ABSTRACT
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.
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CITATION
D. Gunopulos, M. Vazirgiannis, M. Halkidi, N. Kumar, C. Domeniconi, "A Framework for Semi-Supervised Learning Based on Subjective and Objective Clustering Criteria", 2013 IEEE 13th International Conference on Data Mining, vol. 00, no. , pp. 637-640, 2005, doi:10.1109/ICDM.2005.4
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