Current clustering techniques are able to identify arbitrarily shaped clusters in the presence of noise, but depend on carefully chosen model parameters. The choice of model param...
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...