A Framework for Semi-Supervised Learning based on Subjective and Objective Clustering Criteria Maria Halkidi, Dimitrios Gunopulos, Nitin Kumar, Michalis Vazirgiannis, and Carlotta Domeniconi   View Paper (PDF)   Return to Session 10: Learning Techniques I 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. ©2006 Association for Computing Machinery