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-const...
Maria Halkidi, Dimitrios Gunopulos, Nitin Kumar, M...
Advances in wireless networks and positioning technologies (e.g., GPS) have enabled new data management applications that monitor moving objects. In such new applications, realtime...
In this paper, a cluster-based framework is introduced for comparing analysis methods of functional magnetic resonance images (fMRI). In the proposed framework, fMRI data is repla...
Abstract. We generalize traditional goals of clustering towards distinguishing components in a non-parametric mixture model. The clusters are not necessarily based on point locatio...
Stefanie Jegelka, Arthur Gretton, Bernhard Sch&oum...