Clustering ensembles combine different clustering solutions into a single robust and stable one. Most of existing methods become highly time-consuming when the data size turns to ...
Hua et al. have proposed a stable and efficient tracking algorithm called “K-means tracker”[2, 3, 5]. This paper describes an adaptive non-target cluster center selection met...
Abstract. A central problem in the analysis of functional magnetic resonance imaging (fMRI) data is the reliable detection and segmentation of activated areas. Often this goal is a...
Eero Salli, Ari Visa, Hannu J. Aronen, Antti Korve...
We address the problem of the combination of multiple data partitions, that we call a clustering ensemble. We use a recent clustering approach, known as Spectral Clustering, and th...
Spectral clustering is one of the most widely used techniques for extracting the underlying global structure of a data set. Compressed sensing and matrix completion have emerged a...