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