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A Framework for Semi-Supervised Learning Based on Subjective and Objective Clustering Criteria
Houston, Texas November 27-November 30
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2005.4Fifth IEEE International Conference o ...
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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
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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