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

Authors:
M. Halkidi;D. Gunopulos;N. Kumar;M. Vazirgiannis;C. Domeniconi
Affiliations:
University of California at Riverside and Athens University of Economics and Business;University of California at Riverside;University of California at Riverside;Athens University of Economics and Business;George Mason University
Venue:
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Year:
2005

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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.