title: Improving clustering by imposing network information.
abstract: Cluster analysis is one of the most popular data analysis tools in a wide range of applied disciplines. We propose and justify a computationally efficient and straightforward-to-implement way of imposing the available information from networks/graphs (a priori available in many application areas) on a broad family of clustering methods. The introduced approach is illustrated on the problem of a noninvasive unsupervised brain signal classification. This task is faced with several challenging difficulties such as nonstationary noisy signals and a small sample size, combined with a high-dimensional feature space and huge noise-to-signal ratios. Applying this approach results in an exact unsupervised classification of very short signals, opening new possibilities for clustering methods in the area of a noninvasive brain-computer interface.
authors.affiliation: Università della Svizzera Italiana, Via Giuseppe Buffi 13, 6900 Lugano, Switzerland.
authors.forename: Susanne
authors.initials: S
authors.lastname: Gerber
authors.affiliation: Università della Svizzera Italiana, Via Giuseppe Buffi 13, 6900 Lugano, Switzerland.
authors.forename: Illia
authors.initials: I
authors.lastname: Horenko
keywords: EEG; Network; Neuroscience; clustering; finite element method; graph; regularization; time series analysis; unsupervised classification