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

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.

• Type: Conference
• Year: 2005
• Where: ICDM
• Authors: Maria Halkidi, Dimitrios Gunopulos, Nitin Kumar, Michalis Vazirgiannis, Carlotta Domeniconi
• Keywords: Data Mining, ICDM 2005, Semi-supervised Framework, Weighted Euclidean Subspace